Machine learning rediscovers equations governing ocean biogeochemistry

Climate and ocean models use a series of equations to represent complex natural processes. However, the equations used in ...
GitHub

General-Use Nodal Network Solver (GUNNS)

GUNNS is a set of generic C++ math models that combines nodal circuit analysis algorithms with the hydraulic-thermal-electric analogy to model flow circuits (fluid, thermal, electrical). The models ...
GitHub

Simulated Bifurcation for Python

The Simulated Bifurcation (SB) algorithm is a fast and highly parallelizable state-of-the-art algorithm for quadratic combinatorial optimization inspired by quantum physics and spins dynamics. It ...
C&EN

Modular Integration of Python Programming in Undergraduate Physical Chemistry Experiments

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Programming is a key transferable skill within the chemical sciences with applications ...
Simon Fraser University

Solving Linear Systems (NumPy)

Most linear algebra courses start by considering how to solve a system of linear equations. \[ \begin{align} a_{0,0}x_0 + a_{0,1}x_0 + \cdots a_{0,n-1}x_0 & = b_0 ...
Wired

How I Became a Python Programmer—and Fell Out of Love With the Machine

The German historian Oswald Spengler considered our age the age of abstraction. Nowhere is this more apparent than in programming, where abstraction isn’t just a conceptual convenience but an absolute ...
Nature

A neural network-based PDE solving algorithm with high precision

The consumption of solving large-scale linear equations is one of the most critical issues in numerical computation. An innovative method is introduced in this study to solve linear equations based on ...
PNAS

Sensing prior constraints in deep neural networks for solving exploration geophysical problems

We present three possible strategies to effectively incorporate geological and/or geophysical constraints into deep neural networks (DNNs). They help address the main challenges of poor ...
Frontiers

Simulating the Euler equations on multiple GPUs using Python

GPUs have become a household name in High Performance Computing (HPC) systems over the last 15 years. However, programming GPUs is still largely a manual and arduous task, which requires expert ...
acm.org

Neural Networks Learn to Speed Up Simulations

Physical scientists and engineering research and development (R&D) teams are embracing neural networks in attempts to accelerate their simulations. From quantum mechanics to the prediction of blood ...
Frontiers

Convolutional Neural Networks for Very Low-Dimensional LPV Approximations of Incompressible Navier-Stokes Equations

The control of general nonlinear systems is a challenging task in particular for large-scale models as they occur in the semi-discretization of partial differential equations (PDEs) of, say, fluid ...
PNAS

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level

We demonstrate that a neural network automatically solves, explains, and generates university-level problems from the largest Massachusetts Institute of Technology (MIT) mathematics courses at a human ...