Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation - Chris Rackauckas (ASA Statistical Computing & Graphics Sections)

This page summarizes the projects mentioned and recommended in the original post on /r/Julia
Differentialequations sensitivity-analysis neural-ode adjoint backpropogation

WorkOS
Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
Our great sponsors

  • SciMLSensitivity.jl

    A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

    • Plenty of code examples! https://sensitivity.sciml.ai/dev is the main resource, but most of the papers mentioned have their own code repositories. I'm trying to get most of them updated and into the larger SciMLSensitivity docs so they are all tested, though we need new hosting computers to actually get that done.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

    WorkOS logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts