Results 101 to 110 of about 8,116 (238)
PINNs for Electromagnetic Wave Propagation
Physics-Informed Neural Networks (PINNs) solve physical systems by incorporating governing partial differential equations directly into neural network training. In electromagnetism, where well-established methodologies such as FDTD and FEM already exist, new methodologies are expected to provide clear advantages to be accepted.
openaire +2 more sources
Black Humanism: Sameness and Difference in Being Human and Being Black in the United Kingdom
Modern Theology, EarlyView.Abstract This essay outlines a variety of responses to a central question pertaining to Black humanism: namely, what does it mean to be a Black human, given the existence of systemic racism within the Body of Christ and in many White majority societies in the African Diaspora, in this case, within the United Kingdom of Great Britain?
Anthony G. Reddie
wiley +1 more source
Scientific ReportsIn this study, we proposed a weighted loss hard constraint physics-informed neural networks (PINNs) called WHC-PINN. WHC-PINN solves hyperbolic equations with the aid of a gradient weighting approach and by applying hard constraints.
Mahshid Sadat Ghoreishi, Hamid Naderan
doaj +1 more source
From few‐shot learning to data‐efficient intelligence
AI Magazine, Volume 47, Issue 4, Winter 2026.Abstract Modern artificial intelligence (AI) has achieved remarkable progress by scaling data, model size, and computation. Yet scaling alone does not resolve one of the most fundamental gaps between machine and human intelligence: the ability to learn, adapt, and act from limited experience. This limitation is especially visible when labeled examples,
Yaqing Wang
wiley +1 more source
Structure Preserving PINN for Solving Time Dependent PDEs with Periodic Boundary [PDF]
We present a structure preserving PINN for solving a series of time dependent PDEs with periodic boundary. Our method can incorporate the periodic boundary condition as the natural output of any deep neural net, hence significantly improving the training
Wang, Lifan +4 more
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Data‐Driven Spectral Prediction for Accelerating Large‐Scale Electronic Structure Calculations
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.ABSTRACT Simulating large molecular systems comprising thousands of atoms requires highly scalable methodologies. While modern Density Functional Theory (DFT) codes exhibit linear scaling, solving the associated large, sparse generalized eigenproblems remains a critical computational bottleneck on exascale architectures.
Abhiram Badrinarayanan +6 more
wiley +1 more source
Enabling Hybrid Modeling in Commercial MBS Software: A Force‐Level Approach
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.ABSTRACT Hybrid modeling aims to combine physical and data‐driven models to increase simulation accuracy without losing physical interpretability. In the context of dynamic mechanical systems, this enables the compensation of modeling inaccuracies that arise from simplifications, missing effects, or uncertain parameters. In this work, a hybrid model is
Meike Wohlleben +3 more
wiley +1 more source
Scientific ReportsFluid dynamics computations for tube-like geometries are crucial in biomedical evaluations of vascular and airways fluid dynamics. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional computational fluid ...
Hong Shen Wong +3 more
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Global Graph Surrogates and Neural Graph Elements for Computational Mechanics
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.ABSTRACT Machine learning surrogates are increasingly used to accelerate physics‐based simulations by predicting full‐field responses at a fraction of the computational cost. In this contribution, we present two complementary graph‐based approaches for surrogate modelling in computational mechanics.
Rutwik Gulakala, Marcus Stoffel
wiley +1 more source
PINN-BO: A Black-Box Optimization Algorithm Using Physics-Informed Neural Networks
PINN-BO: A Black-Box Optimization Algorithm Using Physics-Informed Neural ...
Sunil Gupta (13096512) +3 more
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