A new article notes that journal articles reporting how well machine learning models solve certain kinds of equations are often overly optimistic. The researchers suggest two rules for reporting ...

The hype surrounding machine learning, a form of artificial intelligence, can make it seem like it is only a matter of time ...

Machine Learning ML offers significant potential for accelerating the solution of partial differential equations (PDEs), a critical area in computational physics. The aim is to generate accurate PDE ...

Bo Nix’s downfield passing remains a problem, Justin Fields gives proof of concept and Derek Carr does very Derek Carr things ...

This new model accounts for a wide range of ion-electrode interactions and predicts a device's ability to store electric ...

A collection of articles, podcasts & tweets from around the web to keep you in touch with the Commanders, the NFC East and ...

termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs) ...

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R ...

Maths for Data Science is an essential pillar of the data-driven industry. Whether you want to analyze complex datasets, ...

The Chargers hope Justin Herbert can play against the Chiefs, but feel lucky to have Taylor Heinicke and Easton Stick in the ...

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...