Sim2Science: ML with Imperfect Scientific Models

About the Workshop

AI4Science has matured into an established field, with ML now embedded throughout the simulator-based workflows of the natural sciences. Much of this progress runs through simulators—mechanistic models hand-crafted by domain experts and fit to data—that encode our scientific theories and underpin prediction, parameter inference, experimentation, and decision-making. Yet an ML method coupled to a simulator is only as good as that simulator: simulators simplify complex systems, omit intractable physics, and depend on uncertain parameters, creating a discrepancy between simulated and observed data that biases the scientific conclusions we draw.

The central question of this workshop is: How can we best leverage imperfect scientific simulators when confronted with real-world data, and how can ML help to account for and mitigate limitations in simulator-based workflows across a wide range of domains? Sim2Science is deliberately cross-domain: rather than focusing on a single scientific field, we bring together researchers who each maintain hierarchies of simulators at different fidelities—in chemistry, fusion, neuroscience, climate, and beyond—to build a shared vocabulary and toolkit for handling imperfect simulators, so that progress in one field can transfer to others.

Topics of Interest

We invite contributions on theory and methods as well as applications spanning biology, chemistry, physics, materials science, climate science, and related fields. Topics include:

• Simulation-based inference and related parameter inference methods
• Understanding and mitigating model misspecification, including simulator diagnostics and discrepancy modeling
• Emulator and surrogate modeling, as well as hybrid and physics-informed approaches
• Analysis of simulator structure, degeneracy, simplifications, and identifiability
• Simulator pipelines, including data handling, preprocessing, and integration with downstream ML models
• Active learning and Bayesian optimization for fitting parameters or model components
• Closed-loop and experiment-in-the-loop scientific workflows
• Multi-fidelity and multi-resolution modeling
• (Agentic) model and equation discovery
• Differentiable frameworks, LLM-assisted scientific reasoning, and workflow automation

Full submission tracks and instructions are on the Call for Papers page.

Sponsors

We gratefully acknowledge confirmed sponsorship from:

Contact

For questions or inquiries about the workshop, please contact us at:
sim2science@gmail.com