Physicists Have Argued Over This Problem Since 1883. It May Now Be Solved.

For more than a century, physicists debated which way a submerged sprinkler sucking in water would spin. Careful experiments ...
Frontiers

Implementing physics-informed neural networks with deep learning for differential equations

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 ...
PNAS

Data-driven equation discovery reveals nonlinear reinforcement learning in humans

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 ...
GitHub

Method of Manufactured Solutions Repository

MASA (Manufactured Analytical Solution Abstraction) is a library written in C++ (with C, python and Fortran90 interfaces) which provides a suite of manufactured solutions for the software verification ...
IEEE

Resilient Adaptive Parallel sImulator for griD (RAPID): An Open Source Power System Simulation Toolbox

Abstract: This paper describes an open source power system simulation toolbox: Resilient Adaptive Parallel sImulator for griD (RAPID), a package of Python codes that implements an advanced power ...
Frontiers

Application of Generalized (Hyper-) Dual Numbers in Equation of State Modeling

The calculation of derivatives is ubiquitous in science and engineering. In thermodynamics, in particular, state properties can be expressed as derivatives of thermodynamic potentials. The manual ...
PNAS

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and ...