Results 91 to 100 of about 2,291 (214)
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
CoRRPhysics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs). However, their performance heavily relies on the strategy used to select training points. Conventional adaptive sampling methods, such as residual-based refinement, often require multi-round sampling and repeated retraining ...
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Philosophical Investigations, Volume 49, Issue 3, Page 279-289, July 2026.ABSTRACT This article examines what it means to respond, or fail to respond, to the individual realities of others in cases of deep moral disagreement concerning trans‐exclusionary sentiments. Building on a limitation we identify in Daniele Moyal‐Sharrock and Constantine Sandis' account of ‘bedrock gender’, we consider two readings of Kendrick Lamar's ...
Ryan Manhire, Salla Aldrin Salskov
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Physics‐Informed Neural Networks for Modeling the Martian Induced Magnetosphere
Geophysical Research Letters, Volume 53, Issue 11, 16 June 2026.Abstract Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics‐based, relying on computationally intensive simulations. For the first time, we
Jiawei Gao +8 more
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Theoretical and Applied Mechanics LettersDynamic wake field information is vital for the optimized design and control of wind farms. Combined with sparse measurement data from light detection and ranging (LiDAR), the physics-informed neural network (PINN) frameworks have recently been employed ...
Junyong Song +3 more
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Seeing Through Scattering With Computational Advances: A Review
Advanced Photonics Research, Volume 7, Issue 6, June 2026.In scattering media, light scrambles into random speckles and impedes our vision. Unlocking hidden information enables breakthroughs to see behind the opaqueness, inspiring applications in imaging, communication, and encryption. Unlike clear media such as clear water and air, a scattering medium is inhomogeneous, in which propagating photons are ...
Huanhao Li +4 more
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Advanced Intelligent Discovery, Volume 2, Issue 3, June 2026.We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
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DDR-PINN: A Dynamic Domain–Gradient Reweighting Physics-Informed Neural Network
Applied SciencesPhysics-informed neural networks (PINNs) solve partial differential equations (PDEs) by embedding physical conditions as soft penalties into the loss function.
Shangpeng Lei +5 more
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Convolution approach to the piNN system
, 1994 18, FIAS-R ...
Blankleider, B., Kvinikhidze, A. N.
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