Results 111 to 120 of about 8,116 (238)
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 ...
openaire +2 more sources
Reconfigurable Computing in Neural Network‐Enhanced Finite Element Analysis in Structural Dynamics
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.ABSTRACT In the present study, artificial neural networks (ANNs) trigger a better physically plausible initial guess in the Newton–Raphson scheme of an implicit material integration scheme of rate‐dependent viscoplastic dynamic response of plate structures in an in‐house finite element (FE) solver.
Vasileios Polydoras +2 more
wiley +1 more source
Field-calibrated PINN-based hybrid approach to predict bottomhole pressure and temperature in CO
Carbon Capture Science & TechnologyAccurately predicting Bottomhole Pressure (BHP) and Bottomhole Temperature (BHT) is important for the safe and efficient operation of Carbon Capture and Storage (CCS) injection systems.
Massiagbe Fatoumata Diabate +2 more
doaj +1 more source
Convolution approach to the piNN system
, 1994 18, FIAS-R ...
Blankleider, B., Kvinikhidze, A. N.
openaire +2 more sources
Trusting Machine Learning With Physics: A Fidelity Verification Framework for Complex Systems
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 6, December 2026.Abstract Neural networks (NN) are embraced to model complex systems based only on data, but their opacity remains a concern. It is unclear whether skill comes from learning physical dependencies or data memorization, which could lead to catastrophic errors.
Sanah Suri, Maike Sonnewald
wiley +1 more source
Reviews of Geophysics, Volume 64, Issue 4, December 2026.Abstract Spatial heterogeneity, manifested as strong variability in hydraulic conductivity (K) within geological formations, exerts a fundamental control on groundwater flow and solute transport. Because K can vary over multiple orders of magnitude in complex three‐dimensional patterns, predicting contaminant plume migration remains a long‐standing ...
Zhilin Guo +8 more
wiley +1 more source
PINNs for Medical Image Analysis: A Survey
CoRRThe incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). By integrating fundamental knowledge and governing physical laws, these models achieve enhanced robustness and interpretability.
Chayan Banerjee +4 more
openaire +2 more sources
Journal of Geophysical Research: Atmospheres, Volume 131, Issue 21, 16 November 2026.Abstract Methane (CH4 ${\text{CH}}_{4}$) is a critical greenhouse gas, and accurate monitoring of its vertical distribution is essential for understanding its radiative impact and identifying emission sources. This paper introduces a novel methodology using a physics‐constrained neural network based on soft constraints to retrieve CH4 ${\text{CH}}_{4}$
Rocco Giosa +12 more
wiley +1 more source
A data‐assisted physics‐informed neural network (DA‐PINN) for fretting fatigue lifetime prediction
In this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of ...
ZHOU, ZHIKUN +6 more
core +1 more source