mccormick.northwestern.edu

ES_APPM 411-2 : Differential Equations of Mathematical Physics

This is the 2nd part of a two course graduate sequence in analytical methods to solve partial differential equations of mathematical physics. Review of Separation of variables. Laplace Equation: ...
Psychology Today

When Math Meets Mind

What if the famous P vs NP problem isn’t just about algorithms but about the observers trying to solve them? Research suggests computational difficulty depends on observer limits.
IEEE

A Fast-Convergence Near-Memory-Computing Accelerator for Solving Partial Differential Equations

Abstract: Solving partial differential equations (PDEs) is omnipresent in scientific research and engineering and requires expensive numerical iteration for memory and computation. The primary ...
IEEE

Analog Computing for Nonlinear Shock Tube PDE Models: Test and Measurement of CMOS Chip

Abstract: Long left ignored by the digital computing industry since its heyday in 1940’s, analog computing is today making a comeback as Moore’s Law slows down. Analog CMOS has power efficiency ...
GitHub

Binary structured physics-informed neural networks for solving equations with rapidly changing solutions

Physics-informed neural networks (PINNs), rooted in deep learning, have emerged as a promising approach for solving partial differential equations (PDEs). By embedding the physical information ...
GitHub

DeepFlow: Physics-Informed Neural Networks for Fluid Dynamics

DeepFlow is a user-friendly framework for solving partial differential equations (PDEs), such as the Navier-Stokes equations, using Physics-Informed Neural Networks (PINNs). It provides a ...