May 2, 2026 · Artificial Intelligence

Finally, Researchers Uncover Deep Learning’s “Newton’s Law”

A new collaborative paper from top universities proposes a unified “Learning Mechanics” framework for deep learning, outlining five research strands—from solvable idealized models and extreme limits to empirical scaling laws and hyper‑parameter theory—while drawing analogies to classical physics and highlighting ten open challenges.

deep learninghyperparameter theorylearning mechanics

0 likes · 16 min read

Finally, Researchers Uncover Deep Learning’s “Newton’s Law”

AI Agent Research Hub

Mar 16, 2026 · Artificial Intelligence

How NTK Adaptive Weighting and Multi‑Scale Fourier Features Enable PINNs to Solve High‑Frequency PDEs

This tutorial explains why standard physics‑informed neural networks fail on high‑frequency partial differential equations due to spectral bias, and demonstrates how random Fourier feature embeddings, multi‑scale concatenation or spatio‑temporal separation, and Neural Tangent Kernel‑based adaptive loss weighting together overcome the bias and achieve accurate, stable solutions for heat, Poisson, and wave equations using JAX.

Fourier FeaturesJAXMulti-Scale

0 likes · 23 min read

How NTK Adaptive Weighting and Multi‑Scale Fourier Features Enable PINNs to Solve High‑Frequency PDEs