Tootfinder

Non-Linear Solar EUV-Driven Sodium Release from the Lunar Surface: A Contrast to the Linear PSD Model
A Devaraj, S Narendranath, Sreeja S Kartha, M Sarantos, Akhil Krishna R, Blesson Mathew, T Sivarani, S Nidhi, P Anbazhagan, G Selvakumar
https://arxiv.org/abs/2509.05644

Non-Linear Solar EUV-Driven Sodium Release from the Lunar Surface: A Contrast to the Linear PSD Model
The correlation between solar Extreme Ultra-Violet (EUV) radiation above 8.8 eV and the release of sodium from the lunar surface via photon-stimulated desorption (PSD) is investigated. We use simultaneous measurements of EUV photon flux and Na optical spectral line flux ($F_{Na}$) from the lunar exosphere. Data were acquired with the high-resolution (R$\sim$72000) Echelle Spectrograph on the 2.34-m Vainu Bappu Telescope during the lunar first quarter (January-March 2024), observing $Na\,I$ D2 a…

Quantum Simulation and Optimization of Water Distribution Networks
Carlos Romero Rocha, Nicolas Renaud, Koen Leijnse, Samuel van Beek, Mario Castro-Gama
https://arxiv.org/abs/2507.06862

Quantum Simulation and Optimization of Water Distribution Networks
To compute models for Water Distribution Networks (WDN), a large system of non-linear equations needs to be solved. The hallmark algorithm for computing these models is the Newton-Raphson Global Gradient Algorithm (NR-GGA), which solves these systems iteratively. Even so, large networks can take multiple days to model and the complexity of networks is only expected to increase in the future. It is therefore important to explore different algorithms using innovative technologies, to improve the …

Mapping Sparse Triangular Solves to GPUs via Fine-grained Domain Decomposition
Atharva Gondhalekar, Kjetil Haugen, Thomas Gibson, Wu-chun Feng
https://arxiv.org/abs/2508.04917 h…

Mapping Sparse Triangular Solves to GPUs via Fine-grained Domain Decomposition
Sparse linear systems are typically solved using preconditioned iterative methods, but applying preconditioners via sparse triangular solves introduces bottlenecks due to irregular memory accesses and data dependencies. This work leverages fine-grained domain decomposition to adapt triangular solves to the GPU architecture. We develop a fine-grained domain decomposition strategy that generates non-overlapping subdomains, increasing parallelism in the application of preconditioner at the expense…

Uncovering the Influence Flow Model of Transistor Amplifiers, Its Reconstruction and Application
Mohammed Tuhin Rana, Mishfad Shaikh Veedu, Murti V. Salapaka
https://arxiv.org/abs/2508.04977

Uncovering the Influence Flow Model of Transistor Amplifiers, Its Reconstruction and Application
Multistage transistor amplifiers can be effectively modeled as network of dynamic systems where individual amplifier stages interact through couplings that are dynamic in nature. Using circuit analysis techniques, we show that a large class of transistor amplifiers can be modeled as Linear Dynamic Influence Model (LDIM), where the interactions between different amplifier stages are modeled as linear dynamic equations. LDIM modeling of transistor circuits leads to application of data-driven netw…

Spacetime Wavelet Method for Linear Boundary-Value Problems in Sylvester Matrix Equation Form
Cody D. Cochran, Karel Matous
https://arxiv.org/abs/2509.02720 https://

Spacetime Wavelet Method for Linear Boundary-Value Problems in Sylvester Matrix Equation Form
We present a high-order spacetime numerical method for discretizing and solving linear initial-boundary value problems using wavelet-based techniques with user-prescribed error estimates. The spacetime wavelet discretization yields a system of algebraic equations resulting in a Sylvester matrix equation. We solve this system with a Global Generalized Minimal Residual (GMRES) method in conjunction with a wavelet-based recursive algorithm to improve convergence. We perform rigorous verification s…

The Landau equation and Fisher information
Nestor Guillen, Luis Silvestre
https://arxiv.org/abs/2507.05167 https://arxiv.org/pdf/2507…

The Landau equation and Fisher information
In this expository note (submitted to Notices of the AMS) we present the ideas used in our recent work ruling out blow up for the Landau equation with Coulomb potential. Blow up is ruled out by the discovery that the Fisher information is not increasing in time along a solution. This monotonicity is established by means of a new ``lifted equation'' which is an auxiliary linear equation in double the number of variables that encodes the nonlinear nonlocal collision operator. For the Landau equat…

Quantum algorithm for linear matrix equations
Rolando D. Somma, Guang Hao Low, Dominic W. Berry, Ryan Babbush
https://arxiv.org/abs/2508.02822 https://arxi…

Quantum algorithm for linear matrix equations
We describe an efficient quantum algorithm for solving the linear matrix equation AX+XB=C, where A, B and C are given complex matrices and X is unknown. This is known as the Sylvester equation, a fundamental equation with applications in control theory and physics. Rather than encoding the solution in a quantum state in a fashion analogous to prior quantum linear algebra solvers, our approach constructs the solution matrix X in a block-encoding, rescaled by some factor. This allows us to obtain…

A New Inexact Manifold Proximal Linear Algorithm with Adaptive Stopping Criteria
Zhong Zheng, Xin Yu, Shiqian Ma, Lingzhou Xue
https://arxiv.org/abs/2508.19234 https://

A New Inexact Manifold Proximal Linear Algorithm with Adaptive Stopping Criteria
This paper studies the nonsmooth and nonconvex composite optimization on Riemannian manifolds. We propose a new inexact manifold proximal linear algorithm (IManPL) that incorporates both high- and low-accuracy conditions. At each iteration, IManPL solves convex subproblems on tangent spaces inexactly, guided by two adaptive stopping criteria. We establish convergence guarantees and show that IManPL achieves the best iteration complexity for solving the nonsmooth manifold composite optimization.…

Solving Markov Chains with Analog Quantum Computing: The Fine Print
Ward van der Schoot, Niels M. P. Neumann
https://arxiv.org/abs/2508.02199 https://arxiv…

Solving Markov Chains with Analog Quantum Computing: The Fine Print
With a growing interest in quantum computing, the number of proposed quantum algorithms grows as well. The practical applicability of these algorithms differs: Some can be applied out-of-the-box, while others require black box oracles, which can not always be easily implemented. One of the first works to explicitly discuss these practical applicability aspects is by Aaronson discussing the \textit{fine print} of the HHL quantum algorithm that solves linear systems of equations. We extend this l…

Data-Driven Motion Planning for Uncertain Nonlinear Systems
Babak Esmaeili, Hamidreza Modares, Stefano Di Cairano
https://arxiv.org/abs/2508.00154 https://…

Data-Driven Motion Planning for Uncertain Nonlinear Systems
This paper proposes a data-driven motion-planning framework for nonlinear systems that constructs a sequence of overlapping invariant polytopes. Around each randomly sampled waypoint, the algorithm identifies a convex admissible region and solves data-driven linear-matrix-inequality problems to learn several ellipsoidal invariant sets together with their local state-feedback gains. The convex hull of these ellipsoids, still invariant under a piece-wise-affine controller obtained by interpolatin…