PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data

dynamicslab/pysindy 17 Apr 2020

PySINDy is a Python package for the discovery of governing dynamical systems models from data.

Dynamical Systems Computational Physics

0.05 stars / hour

Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools

cselab/odil 10 May 2022

We introduce the Optimizing a Discrete Loss (ODIL) framework for the numerical solution of Partial Differential Equations (PDE) using machine learning tools.

Numerical Analysis Numerical Analysis Computational Physics

0.04 stars / hour

Large Sample Properties of Partitioning-Based Series Estimators

nppackages/binsreg 13 Apr 2018

We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, econometrics, and machine learning.

Statistics Theory Econometrics Statistics Theory

0.04 stars / hour

Optimal Routing for Constant Function Market Makers

angeris/cfmm-routing-code 11 Apr 2022

We consider the problem of optimally executing an order involving multiple crypto-assets, sometimes called tokens, on a network of multiple constant function market makers (CFMMs).

Optimization and Control Trading and Market Microstructure

0.03 stars / hour

Computing in Operations Research using Julia

jump-dev/jump.jl 5 Dec 2013

The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome low-level languages such as C, C++, and Fortran and highly expressive yet typically slow high-level languages such as Python and MATLAB.

Optimization and Control Numerical Analysis Programming Languages

0.03 stars / hour

The automatic solution of partial differential equations using a global spectral method

dlfivefifty/ApproxFun.jl 10 May 2015

A spectral method for solving linear partial differential equations (PDEs) with variable coefficients and general boundary conditions defined on rectangular domains is described, based on separable representations of partial differential operators and the one-dimensional ultraspherical spectral method.

Numerical Analysis

0.03 stars / hour

On symmetrizing the ultraspherical spectral method for self-adjoint problems

ApproxFun/ApproxFun.jl 20 Mar 2019

A mechanism is described to symmetrize the ultraspherical spectral method for self-adjoint problems.

Numerical Analysis

0.03 stars / hour

Universal Average-Case Optimality of Polyak Momentum

d2l-ai/d2l-en 19 Aug 2020

Polyak momentum (PM), also known as the heavy-ball method, is a widely used optimization method that enjoys an asymptotic optimal worst-case complexity on quadratic objectives.

Optimization and Control

0.03 stars / hour

ADCME: Learning Spatially-varying Physical Fields using Deep Neural Networks

kailaix/ADCME.jl 24 Nov 2020

We express both the numerical simulations and DNNs using computational graphs and therefore, we can calculate the gradients using reverse-mode automatic differentiation.

Numerical Analysis Numerical Analysis

0.03 stars / hour

A purely hyperbolic discontinuous Galerkin approach for self-gravitating gas dynamics

trixi-framework/Trixi.jl 24 Aug 2020

One of the challenges when simulating astrophysical flows with self-gravity is to compute the gravitational forces.

Numerical Analysis Numerical Analysis Computational Physics

0.03 stars / hour