Connectionists: Call for Papers: 1st workshop on "Differentiable Systems and Scientific Machine Learning" at EurIPS Copenhagen

[Felix Köhler]

- Workshop Website:
https://differentiable-systems.github.io/workshop-eurips-2025/

- December 6 or 7, 2025 as part of EurIPS 2025 at Bella Center,
Copenhagen

- Submission Deadline: 10 October 2025, AOE via
https://openreview.net/group?id=EurIPS.cc/2025/Workshop/DiffSys

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Automatic differentiation is a key technology for most machine learning
models and inverse problems, including surrogate models that simulate +
optimize complex scientific phenomena. But can we go beyond individual
models?

More precisely, can we build entire differentiable systems that combine
multiple components such as adjoint-based simulators, mathematical
solvers, surrogate models, and 3D renderers to tackle real scientific
challenges?

This workshop aims to answer that question by bringing together experts
from around the community. We expect contributions that present
advances in:

- Novel approaches to scientific machine learning (SciML).
- Design, implementation, and analysis of end-to-end differentiable
systems.
- Challenges and solutions when propagating gradients and performing
optimization through complex operations and pipelines.
- Demonstrations of differentiable systems specifically, or SciML
broadly, on real-world scientific problems.

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CFP:

We invite previously unpublished submissions in a form of short papers,
including those describing work in progress, as long as they represent
advances within one or more of the following topics:

    Challenges and opportunities in end-to-end differentiable systems.
    Benchmarks involving SciML components or differentiable systems.
    Differentiable versions of traditionally non-differentiable
operations.
    Theory and practice of automatic differentiation and differentiable
programming.
    Theoretical insight into properties of differentiable systems.
    Differentiable programming in heterogeneous or distributed
computing environments.
    Surrogate modelling (physics-infused methods, neural operators,
...).
    Foundation models for simulation / science.
    Hybrid models combining mechanistic and data-driven components.
    Gradient-based optimization in SciML, e.g. for inverse problems and
parameter estimation.
    Alternative methods of performing optimization (such as Bayesian
optimization).
    Reinforcement learning and optimal control.
    Case studies applying SciML / differentiable systems to a
scientific domain (e.g., weather / climate, physics, engineering
design).