Tootfinder

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- A physics informed neural network approach to simulating ice dynamics governed by the shallow ice...
Kapil Chawla, William Holmes

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- When Influence Misleads: Informational and Strategic Limits of Social Learning in Trading Networks
Bijin Joseph, Christoph Riedl, Alex Pentland, Esteban Moro

Replaced article(s) found for physics.bio-ph. https://arxiv.org/list/physics.bio-ph/new
[1/1]:
- Functional classification of metabolic networks
Jorge Reyes, J\"orn Dunkel

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen
https://arxiv.org/abs/2507.02748

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Transformers have become the de facto standard for a wide range of tasks, from image classification to physics simulations. Despite their impressive performance, the quadratic complexity of standard Transformers in both memory and time with respect to the input length makes them impractical for processing high-resolution inputs. Therefore, several variants have been proposed, the most successful relying on patchification, downsampling, or coarsening techniques, often at the cost of losing the f…

Replaced article(s) found for physics.plasm-ph. https://arxiv.org/list/physics.plasm-ph/new
[1/1]:
- Optical effects in unmagnetized cold plasmas by a chiral axion factor
Filipe S. Ribeiro, Pedro D. S. Silva, Manoel M. Ferreira Jr

Replaced article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Valence photoelectron spectra of thiouracils in the gas phase
Mayer, Titov, Lever, Mehner, Murillo-S\'anchez, Walz, Bozek, Saalfrank, G\"uhr

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations
Jeffrey Kelling, et al.

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- WaterLily.jl: A differentiable and backend-agnostic Julia solver for incompressible viscous flow ...
Gabriel D. Weymouth, Bernat Font

Replaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- Induced Berry Connection and Photonic Spin Hall Effect in Optical Dirac Theory
Lili Yang, Longlong Feng

Replaced article(s) found for physics.app-ph. https://arxiv.org/list/physics.app-ph/new
[1/1]:
- Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
Oquendo, Nadir, Jonuzi, Patra, Sinha, Or\'us, Mugel

Bessler engine startup
https://explorersweb.com/johann-bessler-and-the-greatest-physics-fraud-in-history/

Johann Bessler and The Greatest Physics Fraud in History » Explorersweb
In the 18th century, a man convinced the world's greatest scientists that he had discovered perpetual motion. We still don't know how he brought it off.

Replaced article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new
[1/1]:
- Calibration of a $\Delta$E-E telescope based on CeBr$_3$ scintillator for secondary charged parti...
L. Gesson, et al.

Physics-Informed Neural Network Approach to Quark-Antiquark Color Flux Tube
Wei Kou, Xiaoxuan Lin, Bing'ang Guo, Xurong Chen
https://arxiv.org/abs/2506.03513

Physics-Informed Neural Network Approach to Quark-Antiquark Color Flux Tube
We introduce a physics-informed neural network (PINNs) framework for modelling the spatial distribution of chromodynamic fields induced by quark-antiquark pairs, based on lattice Monte Carlo simulations. In contrast to conventional neural networks, PINNs incorporate physical laws--expressed here as differential equations governing type-II superconductivity--directly into the training objective. By embedding these equations into the loss function, we guide the network to learn physically consist…

Replaced article(s) found for physics.med-ph. https://arxiv.org/list/physics.med-ph/new
[1/1]:
- A dynamic reconstruction and motion estimation framework for cardiorespiratory motion-resolved re...
Hua-Chieh Shao, Xiaoxue Qian, Guoping Xu, Can Wu, Ricardo Otazo, …

This https://arxiv.org/abs/2505.06459 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
Physics-Informed Neural Networks (PINNs) have been widely used to obtain solutions to various physical phenomena modeled as Differential Equations. As PINNs are not naturally equipped with mechanisms for Uncertainty Quantification, some work has been done to quantify the different uncertainties that arise when dealing with PINNs. In this paper, we use a two-step procedure to train Bayesian Neural Networks that provide uncertainties over the solutions to differential equation systems provided by…

Replaced article(s) found for physics.geo-ph. https://arxiv.org/list/physics.geo-ph/new
[1/1]:
- Flowy: High performance probabilistic lava emplacement prediction
Moritz Sallermann, Amrita Goswami, Alejandro Pe\~na-Torres, Rohit Goswami

An Ultra-Cold Mechanical Quantum Sensor for Tests of New Physics
Andraz Omahen, Simon Storz, Marius Bild, Dario Scheiwiller, Matteo Fadel, Yiwen Chu
https://arxiv.org/abs/2507.02653

An Ultra-Cold Mechanical Quantum Sensor for Tests of New Physics
Initialization of mechanical modes in the quantum ground state is crucial for their use in quantum information and quantum sensing protocols. In quantum processors, impurity of the modes' initial state affects the infidelity of subsequent quantum algorithms. In quantum sensors, excitations out of the ground state contribute to the noise of the detector, and their prevalence puts a bound on rare events that deposit energy into the mechanical modes. In this work, we measure the excited-state popu…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Transition of AI Models in dependence of noise
Thomas Seidler, Markus Abel

The Pandora project. II: how non-thermal physics drives bursty star formation and temperate mass-loaded outflows in dwarf galaxies
Sergio Martin-Alvarez, Debora Sijacki, Martin G. Haehnelt, Alice Concas, Yuxuan Yuan, Roberto Maiolino, Risa H. Wechsler, Francisco Rodr\'iguez Montero, Marion Farcy, Mahsa Sanati, Yohan Dubois, Joki Rosdahl, Enrique Lopez-Rodriguez, Susan E. Clark

The Pandora project. II: how non-thermal physics drives bursty star formation and temperate mass-loaded outflows in dwarf galaxies
Dwarf galaxies provide powerful laboratories for studying galaxy formation physics. Their early assembly, shallow gravitational potentials, and bursty, clustered star formation histories make them especially sensitive to the processes that regulate baryons through multi-phase outflows. Using high-resolution, cosmological zoom-in simulations of a dwarf galaxy from \textit{the Pandora suite}, we explore the impact of stellar radiation, magnetic fields, and cosmic ray feedback on star formation, o…

Replaced article(s) found for physics.acc-ph. https://arxiv.org/list/physics.acc-ph/new
[1/1]:
- A Compact Low-level RF Control System for Advanced Concept Compact Electron Linear Accelerator
C. Liu, et al.

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 31163 nodes and 120029 edges.
Tags: Social, Collaborati…

This https://arxiv.org/abs/2505.19099 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning
We present SeePhys, a large-scale multimodal benchmark for LLM reasoning grounded in physics questions ranging from middle school to PhD qualifying exams. The benchmark covers 7 fundamental domains spanning the physics discipline, incorporating 21 categories of highly heterogeneous diagrams. In contrast to prior works where visual elements mainly serve auxiliary purposes, our benchmark features a substantial proportion of vision-essential problems (75\%) that mandate visual information extracti…

Replaced article(s) found for physics.ed-ph. https://arxiv.org/list/physics.ed-ph/new
[1/1]:
- Pisarenko's Formula for the Thermopower
Andrei Novitskii, Takao Mori

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- A Liquid-Nitrogen-Cooled Ca Ion Optical Clock with a Systematic Uncertainty of 4.6E-19
Zhang, Ma, Huang, Han, Hu, Wang, Zhang, Tang, Shi, Guan, Gao

Replaced article(s) found for physics.space-ph. https://arxiv.org/list/physics.space-ph/new
[1/1]:
- CAM-NET: An AI Model for Whole Atmosphere with Thermosphere and Ionosphere Extension
Jiahui Hu, Wenjun Dong

This https://arxiv.org/abs/2411.15111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
We investigate learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs), comparing traditional Multilayer Perceptrons (MLPs) with fixed and trainable activations against Kolmogorov-Arnold Networks (KANs) that employ learnable basis functions. While PINNs effectively incorporate physical laws into the learning process, they suffer from convergence and spectral bias problems, which limit their applicability to problems with rapid…

This https://arxiv.org/abs/2506.00951 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Physics-Informed Neural Networks for the Relativistic Burgers Equation in the Exterior of a Schwarzschild Black Hole
We introduce a Physics-Informed Neural Networks(PINN) to solve a relativistic Burgers equation in the exterior domain of a Schwarzschild black hole. Our main contribution is a PINN architecture that is able to simulate shock wave formations in such curved spacetime, by training a shock- aware network block and introducing a Godunov-inspired residuals in the loss function. We validate our method with numerical experiments with different kinds of initial conditions. We show its ability to reprodu…

This https://arxiv.org/abs/2504.09460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Imprints of extra dimensions in eccentric eccentric extreme-mass-ratio inspirals gravitational waveforms
Studies regarding extra-dimensions have been of great interest in modern theoretical physics, including their observational consequences from future gravitational wave (GW) observatories. In this direction, extreme-mass-ratio inspirals (EMRIs), attracting considerable interest in GW astronomy and fundamental physics, can potentially provide a useful platform for the search of extra dimensions. In this paper, we examine a rotating braneworld black hole in the context of equatorial eccentric EMRI…

Sound Field Reconstruction Using Physics-Informed Boundary Integral Networks
Stefano Damiano, Toon van Waterschoot
https://arxiv.org/abs/2506.03917 https:/…

Sound Field Reconstruction Using Physics-Informed Boundary Integral Networks
Sound field reconstruction refers to the problem of estimating the acoustic pressure field over an arbitrary region of space, using only a limited set of measurements. Physics-informed neural networks have been adopted to solve the problem by incorporating in the training loss function the governing partial differential equation, either the Helmholtz or the wave equation. In this work, we introduce a boundary integral network for sound field reconstruction. Relying on the Kirchhoff-Helmholtz bo…

from my link log —
How common is multiple invention?
https://www.construction-physics.com/p/how-often-do-inventions-have-multiple
saved 2025-06-06

How Common is Multiple Invention?
When Alexander Graham Bell filed a patent for the telephone on February 14th, 1876, he beat competing telephone developer Elisha Gray to the patent office by just a few hours.

This https://arxiv.org/abs/2506.00593 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally prohibitive for large-scale screening. We propose a hybrid approach that approximates the accuracy of physics-based methods by training targ…

PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis
Mijeong Kim, Gunhee Kim, Jungyoon Choi, Wonjae Roh, Bohyung Han
https://arxiv.org/abs/2506.02794

PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis
We introduce PhysGaia, a novel physics-aware dataset specifically designed for Dynamic Novel View Synthesis (DyNVS), encompassing both structured objects and unstructured physical phenomena. Unlike existing datasets that primarily focus on photorealistic reconstruction, PhysGaia is created to actively support physics-aware dynamic scene modeling. Our dataset provides complex dynamic scenarios with rich interactions among multiple objects, where they realistically collide with each other and exc…

My JS Krups is resisting netbooting. I see it doing a DHCP discover, and I see Kea responding with an offer, but then it just sends another Discover. Hmph. I can get to the serial console and doing boot net from there doesn't help; none of the keyboard shortcuts for network diag etc seem to work (except the one that displays the help for it...). So I took the flash SIM out and now it boots to Net rather than flash by default; alas with the same DHCP behaviour. Time to try isc-dhcp.…

Replaced article(s) found for hep-th. https://arxiv.org/list/hep-th/new
[2/2]:
- X-Ray Reflection Signatures of Supermassive Black Hole Binaries
Julie Malewicz, David R. Ballantyne, Tamara Bogdanovi\'c, Laura Brenneman, Thomas Dauser

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks
Ashutosh Kumar Mishra, Emma Tolley
https://arxiv.org/abs/2506.02957

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astrophysics and cosmology. We introduce the Schrödinger-Poisson informed neural network (SPINN) which solve nonlinear Schrödinger-Poisson (SP) equations to simulate the gravitational collapse of Fuzzy Dark Matter (FDM) in both 1D and 3D settings…

Replaced article(s) found for math-ph. https://arxiv.org/list/math-ph/new
[1/1]:
- Quadratic Hamiltonians in Fermionic Fock Spaces
Jean-Bernard Bru, Nathan Metraud

Optical Physics-Based Generative Models
Amirreza Ahmadnejad, Somayyeh Koohi
https://arxiv.org/abs/2506.04357 https://arxiv.org/pdf/25…

Optical Physics-Based Generative Models
This paper establishes a comprehensive mathematical framework connecting optical physics equations to generative models, demonstrating how light propagation dynamics inspire powerful artificial intelligence approaches. We analyze six fundamental optical equations, comparing linear models (Helmholtz, dissipative wave, and Eikonal equations) with their nonlinear extensions incorporating Kerr effects, cubic-quintic nonlinearities, and intensity-dependent refractive indices. Our nonlinear optical m…

A generative modeling / Physics-Informed Neural Network approach to random differential equations
Georgios Arampatzis, Stylianos Katsarakis, Charalambos Makridakis
https://arxiv.org/abs/2507.01687

A generative modeling / Physics-Informed Neural Network approach to random differential equations
The integration of Scientific Machine Learning (SciML) techniques with uncertainty quantification (UQ) represents a rapidly evolving frontier in computational science. This work advances Physics-Informed Neural Networks (PINNs) by incorporating probabilistic frameworks to effectively model uncertainty in complex systems. Our approach enhances the representation of uncertainty in forward problems by combining generative modeling techniques with PINNs. This integration enables in a systematic fas…

This https://arxiv.org/abs/2505.00947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Summary Report of the Physics Beyond Colliders Study at CERN
The Physics Beyond Collider (PBC) Study Group was initially mandated by the CERN Management to prepare the previous European Particle Physics Strategy Update for CERN projects other than the high-energy frontier colliders. The main findings were summarized in an PBC Summary Report submitted to the Strategy Update. Following the Update process, the PBC Study Group was confirmed on a permanent basis with an updated mandate taking into account the strategy recommendations. The Study Group is now i…

[2025-06-05 Thu (UTC), 1 new article found for physics.hist-ph History and Philosophy of Physics]
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SubMIT: A Physics Analysis Facility at MIT
Josh Bendavid (Massachusetts Institute of Technology, CERN), Mariarosaria D'Alfonso (Massachusetts Institute of Technology), Jan Eysermans (Massachusetts Institute of Technology), Chad Freer (Massachusetts Institute of Technology), Maxim Goncharov (Massachusetts Institute of Technology), Matthew Heine (Massachusetts Institute of Technology), Luca Lavezzo (Massachusetts Institute of Technology), Marianne Moore (Massachusetts Institute of Te…

SubMIT: A Physics Analysis Facility at MIT
The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated d…

This https://arxiv.org/abs/2505.00849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TherMod Communication: Low Power or Hot Air?
The Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme leverages statistical physics to enable secure communication with zero average power flow in a wired channel. While the original KLJN scheme requires significant power for operation, a recent wireless modification, TherMod, proposed by Basar claims a "low power" implementation. This paper critically examines this claim. We explain that the additional components inherent in Basar's wireless adaptation substantially increase power …

Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks
Martina Rama, Gabriele Santin, Giulia Cencetti, Michele Tizzoni, Bruno Lepri
https://arxiv.org/abs/2506.03897

Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks
Accurate epidemic forecasting is critical for informing public health decisions and timely interventions. While Physics-Informed Neural Networks (PINNs) have shown promise in various scientific domains, their application to real-time epidemic forecasting remains limited. The reasons are mainly due to the intrinsic difficulty of the task and the tendency to fully leveraging their learning and inference potential, which, however, often results in non-optimal forecasting frameworks. Here, we prese…

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- PhyLiNO: A Forward-Folding Likelihood-Fit Framework for Neutrino Oscillation Physics
Denise Hellwig, Stefan Schoppmann, Philipp Soldin, Achim Stahl, Christopher Wi…

Plasma sheath physics: A circuital description, amelioration, and application
Pralay Kumar Karmakar, Subham Dutta, Utpal Deka
https://arxiv.org/abs/2506.03935

Plasma sheath physics: A circuital description, amelioration, and application
A synoptic review of the electrical circuital model-based analysis of laboratory plasma sheaths, alongside their stability features in a realistic broader horizon, is systematically presented herein. It explains the basic physics responsible for the inductive (L_sh), capacitive (C_sh), and resistive (R_sh) properties simultaneously, exhibited by plasma sheaths. The analyzed model sheath behaviors are judiciously described in the light of the state-of-the-art sheath scenarios, illustratively. Th…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Assessment of Abrupt Shifts in CMIP6 Models using Edge Detection
Terpstra, Falkena, Bastiaansen, Bathiany, Dijkstra, von der Heydt

Replaced article(s) found for physics.bio-ph. https://arxiv.org/list/physics.bio-ph/new
[1/1]:
- Residual U-Net for accurate and efficient prediction of hemodynamics in two-dimensional asymmetri...
Xintong Zou, Suiyang Tong, Wenhui Peng, Qiuxiang Huang, Jianchun …

Replaced article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- A comprehensive theory for relativistic polaritonic chemistry: a four components ab initio treatm...
Guillaume Thiam, Riccardo Rossi, Henrik Koch, Leonardo Belpass…

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- A Transfer Matrix for the Input Impedance of weakly tapered Cones as of Wind Instruments
Timo Grothe, Johannes Baumgart, Cornelis J. Nederveen

This https://arxiv.org/abs/2504.09460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Imprints of extra dimensions in eccentric eccentric extreme-mass-ratio inspirals gravitational waveforms
Studies regarding extra-dimensions have been of great interest in modern theoretical physics, including their observational consequences from future gravitational wave (GW) observatories. In this direction, extreme-mass-ratio inspirals (EMRIs), attracting considerable interest in GW astronomy and fundamental physics, can potentially provide a useful platform for the search of extra dimensions. In this paper, we examine a rotating braneworld black hole in the context of equatorial eccentric EMRI…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
Oquendo, Nadir, Jonuzi, Patra, Sinha, Or\'us, Mugel

Replaced article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new
[1/1]:
- Genetic algorithm as a tool for detection setup optimisation: SiFi-CC case study
Kasper, Awal, Hetzel, Ko{\l}odziej, Rusiecka, Stahl, Wong, Wro\'nska

A Fast, Accurate and Oscillation-free Spectral Collocation Solver for High-dimensional Transport Problems
Nicola Cavallini, Gianmarco Manzini, Daniele Funaro, Andrea Favalli
https://arxiv.org/abs/2506.04732

A Fast, Accurate and Oscillation-free Spectral Collocation Solver for High-dimensional Transport Problems
Transport phenomena-describing the movement of particles, energy, or other physical quantities-are fundamental in various scientific disciplines, including nuclear physics, plasma physics, astrophysics, engineering, and the natural sciences. However, solving the associated seven-dimensional transport equations poses a significant computational challenge due to the curse of dimensionality. We introduce the Tensor Train Superconsistent Spectral (T${^2}$S${^2}$) solver to address this challeng…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Charting multidimensional ideological polarization across demographic groups in the United States
Jaume Ojer, David C\'arcamo, Romualdo Pastor-Satorras, Michele S…

Replaced article(s) found for physics.ed-ph. https://arxiv.org/list/physics.ed-ph/new
[1/1]:
- Evaluating GPT- and Reasoning-based Large Language Models on Physics Olympiad Problems: Surpassin...
Paul Tschisgale, Holger Maus, Fabian Kieser, Ben Kroehs, Stefan Pete…

Replaced article(s) found for physics.med-ph. https://arxiv.org/list/physics.med-ph/new
[1/1]:
- Genetic algorithm as a tool for detection setup optimisation: SiFi-CC case study
Kasper, Awal, Hetzel, Ko{\l}odziej, Rusiecka, Stahl, Wong, Wro\'nska

Replaced article(s) found for hep-th. https://arxiv.org/list/hep-th/new
[1/2]:
- New Insights into Superintegrability from Unitary Matrix Models
A. Mironov, A. Morozov, Z. Zakirova

This https://arxiv.org/abs/2505.10930 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models
Auto-regressive partial differential equation (PDE) foundation models have shown great potential in handling time-dependent data. However, these models suffer from the shortcut problem deeply rooted in auto-regressive prediction, causing error accumulation. The challenge becomes particularly evident for out-of-distribution data, as the pretraining performance may approach random model initialization for downstream tasks with long-term dynamics. To deal with this problem, we propose physics-info…

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
Elmira Mirzabeigi, Rezvan Salehi, Kourosh Parand
https://arxiv.org/abs/2506.04354

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINNs, which typically rely on fully connected architectures, often struggle to capture complex spatial hierarchies and enforce intricate boundary conditions. In contrast, BridgeNet leverages adaptive CNN layers for effective local feature extraction and incorporates a dynamically w…

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa RYu
https://arxiv.org/abs/2506.00056

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and in…

Replaced article(s) found for physics.geo-ph. https://arxiv.org/list/physics.geo-ph/new
[1/1]:
- Retrieving snow depth distribution by downscaling ERA5 Reanalysis with ICESat-2 laser altimetry
Zhihao Liu, Simon Filhol, D\'esir\'ee Treichler

[2025-06-05 Thu (UTC), 2 new articles found for physics.atom-ph Atomic Physics]
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[2025-06-05 Thu (UTC), no new articles found for physics.space-ph Space Physics]
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Replaced article(s) found for hep-ph. https://arxiv.org/list/hep-ph/new
[1/1]:
- Nonperturbative Corrections to Soft Drop Jet Mass
Andr\'e H. Hoang, Sonny Mantry, Aditya Pathak, Iain W. Stewart

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- A new approach to handling factorial moment correlations through principal component analysis
Nikolaos Davis

[2025-06-05 Thu (UTC), 4 new articles found for physics.app-ph Applied Physics]
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[2025-06-06 Fri (UTC), 1 new article found for physics.hist-ph History and Philosophy of Physics]
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Replaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- Scalability of On-chip Diffractive Optical Neural Networks
Sanaz Zarei

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/2]:
- Stability and quasi-Periodicity of Many-Body Localized Dynamics
Peyman Azodi, Herschel A. Rabitz

[2025-06-05 Thu (UTC), 1 new article found for physics.ao-ph Atmospheric and Oceanic Physics]
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[2025-06-06 Fri (UTC), 1 new article found for physics.bio-ph Biological Physics]
#toXiv_bot_toot

[2025-06-05 Thu (UTC), 8 new articles found for physics.plasm-ph Plasma Physics]
#toXiv_bot_toot

[2025-06-05 Thu (UTC), 5 new articles found for physics.chem-ph Chemical Physics]
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This https://arxiv.org/abs/2505.24584 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams
Recent advancements in generative AI have accelerated the discovery of novel chemicals and materials; however, transitioning these discoveries to industrial-scale production remains a critical bottleneck, as it requires the development of entirely new chemical manufacturing processes. Current AI methods cannot auto-generate PFDs or PIDs, despite their critical role in scaling chemical processes, while adhering to engineering constraints. We present a closed loop, physics aware framework for the…

[2025-06-05 Thu (UTC), 1 new article found for physics.ed-ph Physics Education]
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The QTF-Backbone: Proposal for a Nationwide Optical Fibre Backbone in Germany for Quantum Technology and Time and Frequency Metrology
Klaus Blaum (Max Planck Institute for Nuclear Physics), Peter Kaufmann (German National Research,Education Network, DFN), Jochen Kronj\"ager (Physikalisch-Technische Bundesanstalt), Stefan K\"uck (Physikalisch-Technische Bundesanstalt), Tara Cubel Liebisch (Physikalisch-Technische Bundesanstalt), Dieter Meschede (University of Bonn), Susanne Na…

The QTF-Backbone: Proposal for a Nationwide Optical Fibre Backbone in Germany for Quantum Technology and Time and Frequency Metrology
The recent breakthroughs in the distribution of quantum information and high-precision time and frequency (T&F) signals over long-haul optical fibre networks have transformative potential for physically secure communications, resilience of Global Navigation Satellite Systems (GNSS) and fundamental physics. However, so far these capabilities remain confined to isolated testbeds, with quantum and T&F signals accessible, for example in Germany, to only a few institutions. We propose the QTF-Back…

[2025-06-06 Fri (UTC), 6 new articles found for physics.atom-ph Atomic Physics]
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[2025-06-06 Fri (UTC), no new articles found for physics.space-ph Space Physics]
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Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Large-eddy simulation of turbulent spray flames: Effects of scalar correlation and enthalpy reduc...
Dong Wang, Min Zhang, Ruixin Yang, Zhi X. Chen

[2025-06-06 Fri (UTC), 8 new articles found for physics.soc-ph Physics and Society]
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Attosecond physics in optical near fields
Jonas Heimerl, Stefan Meier, Anne Herzig, Felix L\'opez Hoffmann, Lennart Seiffert, Daniel Lesko, Simon Hillmann, Simon Wittigschlager, Tobias Weitz, Thomas Fennel, Peter Hommelhoff
https://arxiv.org/abs/2507.02673

Attosecond physics in optical near fields
Attosecond science, the electron control by the field of ultrashort laser pulses, is maturing into lightfield-driven electronics, called petahertz electronics. Based on optical field-driven nanostructures, elements for petahertz electronics have been demonstrated. These hinge on the understanding of the electron dynamics in the optical near field of the nanostructure. Here we show near field-induced low energy stripes (NILES) in carrier-envelope phase-dependent electron spectra, a new spectral …

[2025-06-06 Fri (UTC), 5 new articles found for physics.app-ph Applied Physics]
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[2025-06-06 Fri (UTC), no new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
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[2025-06-06 Fri (UTC), 4 new articles found for physics.plasm-ph Plasma Physics]
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[2025-06-05 Thu (UTC), 4 new articles found for physics.bio-ph Biological Physics]
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[2025-06-06 Fri (UTC), 5 new articles found for physics.comp-ph Computational Physics]
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[2025-06-06 Fri (UTC), 1 new article found for physics.ed-ph Physics Education]
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Replaced article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Computing solvation free energies of small molecules with first principles accuracy
J. Harry Moore, Daniel J. Cole, Gabor Csanyi

[2025-06-05 Thu (UTC), 5 new articles found for physics.soc-ph Physics and Society]
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Bayesian frequency estimation at the fundamental quantum limit
James W. Gardner, Tuvia Gefen, Ethan Payne, Su Direkci, Sander M. Vermeulen, Simon A. Haine, Joseph J. Hope, Lee McCuller, Yanbei Chen
https://arxiv.org/abs/2507.02811

Bayesian frequency estimation at the fundamental quantum limit
Searching for a weak signal at an unknown frequency is a canonical task in experiments probing fundamental physics such as gravitational-wave observatories and ultra-light dark matter haloscopes. These state-of-the-art sensors are limited by quantum noise arising from the fundamental uncertainty about the state of the device. Classically, frequency estimation suffers from a threshold effect in the signal-to-noise ratio such that weak signals are extremely hard to localise in frequency. We show …

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
ChangJae Lee, Heecheol Yang, Byeonggwon Kim
https://arxiv.org/abs/2506.03842

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
Accurate quantitative precipitation forecasting (QPF) remains one of the main challenges in numerical weather prediction (NWP), primarily due to the difficulty of representing the full complexity of atmospheric microphysics through parameterization schemes. This study introduces a deep learning-based post-processing model, DL-QPF, which diagnoses precipitation fields from meteorological forecasts by learning directly from high-resolution radar estimates precipitation. The DL-QPF model is constr…

Virtual Polarization Elements for Spatially Varying Jones Matrix Transformations on a Free-Space Plane
Mingyue Wang (Department of Physics, Xiamen University, Xiamen, China), Jiayuan Wang (Department of Physics, Xiamen University, Xiamen, China, Shenzhen Research Institute of Xiamen University, Shenzhen, China)
https://arxiv.org…

Virtual Polarization Elements for Spatially Varying Jones Matrix Transformations on a Free-Space Plane
Precise control over the spatial and polarization properties of light is foundational for advanced photonic systems, yet most conventional approaches are constrained to local, contact-based manipulation at physical interfaces. To overcome these constraints, here we introduce a fundamentally new framework for action-at-a-distance polarization control using virtual polarization elements (VPEs). VPEs apply prescribed local Jones matrix transformations between an input field at the modulation plane…

Replaced article(s) found for physics.bio-ph. https://arxiv.org/list/physics.bio-ph/new
[1/1]:
- The modelling of the action potentials in myelinated nerve fibres
Kert Tamm, Tanel Peets, J\"uri Engelbrecht

Replaced article(s) found for physics.plasm-ph. https://arxiv.org/list/physics.plasm-ph/new
[1/1]:
- 2D electron density profile evolution during detachment in Super-X divertor L-mode discharges on ...
N. Lonigro, et al.

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- SECRET: Stochasticity Emulator for Cosmic Ray Electrons
Nikolas Frediani, Michael Kr\"amer, Philipp Mertsch, Kathrin Nippel

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Active Inference AI Systems for Scientific Discovery
Karthik Duraisamy

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
Mahmoud Mbarak, Manmeet Singh, Naveen Sudharsan, Zong-Liang Yang

This https://arxiv.org/abs/2505.04627 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Is the end of Insight in Sight ?
The rise of deep learning challenges the longstanding scientific ideal of insight - the human capacity to understand phenomena by uncovering underlying mechanisms. In many modern applications, accurate predictions no longer require interpretable models, prompting debate about whether explainability is a realistic or even meaningful goal. From our perspective in physics, we examine this tension through a concrete case study: a physics-informed neural network (PINN) trained on a rarefied gas dyna…

Replaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- Stochastic Equilibrium Raman Spectroscopy (STERS)
Colburn Cobb-Bruno, Hendrik Utzat

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- A New World Framework: The Three Realms and Six Layers Model
Dacheng Zhou