Tootfinder

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
https://arxiv.org/abs/2506.06001

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are desig…

Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
Arishi Orra, Aryan Bhambu, Himanshu Choudhary, Manoj Thakur, Selvaraju Natarajan
https://arxiv.org/abs/2505.03760

Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. I…

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
https://arxiv.org/abs/2506.06021

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Multi-solid systems are foundational to a wide range of real-world applications, yet modeling their complex interactions remains challenging. Existing deep learning methods predominantly rely on implicit modeling, where the factors influencing solid deformation are not explicitly represented but are instead indirectly learned. However, as the number of solids increases, these methods struggle to accurately capture intricate physical interactions. In this paper, we introduce a novel explicit mod…

Sonnet 133 - CXXXIII
Beshrew that heart that makes my heart to groan
For that deep wound it gives my friend and me!
Is't not enough to torture me alone,
But slave to slavery my sweet'st friend must be?
Me from myself thy cruel eye hath taken,
And my next self thou harder hast engrossed:
Of him, myself, and thee I am forsaken;
A torment thrice three-fold thus to be crossed.
Prison my heart in thy steel bosom's ward,
But t…

An Array Decomposition Method for Finite Arrays with Electrically Connected Elements for fast Toeplitz Solvers
Lucas {\AA}kerstedt, Harald Hultin, B. L. G. Jonsson
https://arxiv.org/abs/2506.04710

An Array Decomposition Method for Finite Arrays with Electrically Connected Elements for fast Toeplitz Solvers
A large part of the geometry of array antennas is often partially defined by finite translational symmetries. Applying the method of moments (MoM) with the RWG-like element on an appropriately structured mesh to these arrays results in an impedance matrix where the main part exhibits a multilevel block Toeplitz structure. This article introduces a memory-efficient construction method that effectively represents and reuses impedance calculations. The proposed method, applicable to electrically c…

Hybrid deep learning and iterative methods for accelerated solutions of viscous incompressible flow
Heming Bai, Xin Bian
https://arxiv.org/abs/2506.03016 h…

Hybrid deep learning and iterative methods for accelerated solutions of viscous incompressible flow
The pressure Poisson equation, central to the fractional step method in incompressible flow simulations, incurs high computational costs due to the iterative solution of large-scale linear systems. To address this challenge, we introduce HyDEA, a novel framework that synergizes deep learning with classical iterative solvers. It leverages the complementary strengths of a DeepONet - capable of capturing large-scale features of the solution - and the CG or a PCG method, which efficiently resolves …

Test: Solarbank 3 Pro - Strom vom Balkon: Was Kraftwerk wirklich bringt #News #Nachrichten

Two US disinformation experts have launched a DIY news outlet.
Not on Mastodon yet though. Missing a trick methinks.
https://www.poynter.org/fact-checking/2025/indicator-alexios-mantzarlis-craig-silverman/

Two disinformation experts have launched a DIY news outlet - Poynter
With Indicator, Alexios Mantzarlis and Craig Silverman bring a deep well of experience fighting falsehoods into a chaotic and cynical online world

Moin liebe Alle.
Ich möchte mich gerne von Adobe verabschieden und aus meinem Lightroom Classic und Photoshop Abo raus. LR hat dabei Priorität.
Eventuell hat ja schon einmal jemand den Umstieg ausprobiert und mag seine Erfahrungen teilen. Die KI habe ich natürlich schon befragt, aber die hat das nicht selber gemacht ;)
Im Folge-Posts habe ich meine Anforderungen einmal zusammengefasst:
#Adobe

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Xiantao Fan, Xinyang Liu, Meng Wang, Jian-Xun Wang
https://arxiv.org/abs/2505.23940

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions, and high computational demands. Traditional CFD solvers, though effective, struggle with scalability and adaptability for tasks such as inverse modeling, optimization, and data assimilation. Recent advances in machine learning (ML) have inspired hybrid modelin…