Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

A Python library for optical simulations of multilayer structures using the transfer-matrix method, extended to support nonlinear processes (SHG, SFG, DFG) and Gaussian beam propagation. See also: ...

This work introduces a model-agnostic framework for training and inference to enable accurate partial differential equation solving (down to double precision) for problems with arbitrary sizes and ...

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution ...

Our article offers an answer to a foundational question in psychology and neuroscience: how do people learn from rewards and punishments? Specifically, we introduce a computational model of human ...

Please cite as: Barba, Lorena A., and Forsyth, Gilbert F. (2018). CFD Python: the 12 steps to Navier-Stokes equations. Journal of Open Source Education, 1(9), 21 ...

I'm not sure if you understand how many physicists watch baseball, but it’s a lot. I think it’s so popular with us because there are some very basic principles at work. You can model the motion of a ...