Differentiable Programming Part 1

Differentiable Programming Part 1 Information Guide

1. Background to Differentiable Programming Part 1
2. Core Information
3. Developments
4. Expert Insights
5. Future Outlook

Background to Differentiable Programming Part 1

In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. For more information about Stanford's Artificial Intelligence professional and graduate programs visit: Today we're joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and ... Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large ... In this insightful talk, Valentin Churavy (University of Augsburg) explores The Neuro Symbolic Channel provides the tutorials, courses, and research results on

Talk from HSF/IRIS-HEP Analysis Ecosystem 2 Workshop (

Core Information

Developments

Differentiable Programming for Oceanography with Patrick Heimbach - #557

Models as Code: Differentiable Programming with Zygote

A Simple Differentiable Programming Language

FerriteCon 2025 Valentin Churavy: Differentiable programming for scientific computing

Differentiable Programming for Spatial AI: Representation, Reasoning, and Planning | Krishna Murthy

Differentiable ILP Pt 1: Introduction

Differentiable Programming in HEP

Differentiable Programming with Julia by Mike Innes

Uncertainty Programming: Differentiable Programming Extended to Uncertainty Quantification

Expert Insights

Data is compiled from public records and verified media reports.

Last Updated: June 16, 2026

Future Outlook

Disclaimer: Disclaimer: Details estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.