Sim2Science: ML with Imperfect Scientific Models

AI4Science has matured into an established field, with ML now embedded throughout the simulator-based workflows of the natural sciences. But all models are wrong: every simulator approximates reality. An ML method coupled to a simulator is only as reliable as that simulator, potentially steering us into wrong scientific conclusions and real-world decisions even if the ML component was perfect. This problem of simulator misspecification is central to scientific ML, and adversely impacts fields as different as chemistry, fusion, and neuroscience. Yet the communities that face it rarely meet, and a solution found in one field seldom reaches another. Sim2Science is organized around this shared problem rather than a single domain: it convenes researchers across the sciences and ML to detect, quantify, and mitigate simulator misspecification, and to turn advances in one field into methods that transfer to others.