Accepted Workshop Papers

  1. A Research Framework for Writing Differentiable PDE Discretizations in JAX, by Antonio Stanziola, Simon Arridge, Ben T. Cox, and Bradley Ernest Treeby
  2. Backpropagation through Back Substitution with a Backslash, by Ekin Akyuerek, Alan Edelman, and Bernie Wang
  3. Differentiable Scripting, by Uwe Naumann
  4. Equinox: Neural Networks in JAX via Callable PyTrees and Filtered Transformations, by Patrick Kidger, and Cristian Garcia
  5. Aggregated Type Handling in AD Tape Implementations, by Max Sagebau, and Nicolas R. Gauger
  6. AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia, by Frank Schaefer, Mohamed Tarek, Lyndon White, and Christopher Rackauckas
  7. Escaping the Abstraction: A Foreign Function Interface for a Unified Form Language [UFL], by Nacime Bouziani, and David Ham
  8. A Fully-Differentiable Compressible High-Order Computational Fluid Dynamics Solver, by Deniz Bezgin, Aaron Buhendwa, and Nikolaus Adams
  9. A Complete Axiomatization of Forward Differentiation, by Gordon Plotkin
  10. Neural Differentiable Predictive Control, by Jan Drgona, Aaron Tuor, and Draguna L. Vrabie
  11. Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages, by Alexander K. Lew, Mathieu Huot, and Vikash Mansinghka
  12. On Automatic Differentiation for the Matern Covariance, by Oana Marin, Christopher Geoga, Michel Schanen, and Paul Hovland
  13. Differentiable Parametric Optimization Approach to Power System Load Modeling, by Jan Drgona, Shirang Abhyankar, Andrew August, Aaron Tuor, and Elliott Skomski
  14. Gradients of the Big Bang: Solving the Einstein-Boltzmann Equations with Automatic Differentiation, by James M. Sullivan, and Zack Li
  15. Unbiased Reparametrisation Gradient via Smoothing and Diagonalisation, by Dominik Wagner, and Luke Ong
  16. Generalizability of Density Functionals Learned from Differentiable Programming on Weakly Correlated Spin-Polarized Systems, by Bhupalee Kalita, Ryan Pederson, Li Li, and Kieron Burke
  17. GPU Accelerated Automatic Differentiation with Clad, by Vassil Vassilev, Ioana Ifrim, David Lange, Baidyanath Kundu, and Parth Arora
  18. Extended Abstract - Enzyme.jl: Low-Level Auto-Differentiation meets High-Level Language, by Valentin Churavy