aiqbench analysis
This paper proposes Adversarial Distribution Alignment, a method that pre-trains a generative model on simulated data and then adapts it to partial experimental observations, with a theoretical claim that it recovers the target observable distribution. The validation spans synthetic, molecular, and protein datasets, but the evidence is still at the level of a preprint. Readers working on scientific ML should watch whether the approach holds up on harder systems and whether the theoretical guarantees rely on assumptions that real experiments may not meet.
Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
arXiv:2604.01169v2 Announce Type: replace Abstract: A fundamental challenge in science and engineering is the simulation-to-experiment gap. While we often possess prior knowledge of physical laws, these physical laws can be too difficult to solve exactly for complex systems. Such systems are commonly modeled using simulators, which impose computational approximations. Meanwhile, experimental measurements more faithfully represent the real world, but experimental data typically consists of observations that only partially reflect the system's full underlying state. We propose a data-driven distribution alignment framework that bridges this simulation-to-experiment gap by pre-training a generative model on fully observed (but imperfect) simulation data, then aligning it with partial (but real) observations of experimental data. While our method is domain-agnostic, we ground our approach in the physical sciences by introducing Adversarial Distribution Alignment (ADA). This method aligns a generative model of atomic positions -- initially trained on a simulated Boltzmann distribution -- with the distribution of experimental observations. We prove that our method recovers the target observable distribution, even with multiple, potentially correlated observables. We also empirically validate our framework on synthetic, molecular, and experimental protein data, demonstrating that it can align generative models with diverse observables. Our code is available at https://kaityrusnelson.com/ada/.
Abstract:A fundamental challenge in science and engineering is the simulation-to-experiment gap. While we often possess prior knowledge of physical laws, these physical laws can be too difficult to solve exactly for complex systems. Such systems are commonly modeled using simulators, which impose computational approximations. Meanwhile, experimental measurements more faithfully represent the real world, but experimental data typically consists of observations that only partially reflect the system's full underlying state. We propose a data-driven distribution alignment framework that bridges this simulation-to-experiment gap by pre-training a generative model on fully observed (but imperfect) simulation data, then aligning it with partial (but real) observations of experimental data. While our method is domain-agnostic, we ground our approach in the physical sciences by introducing Adversarial Distribution Alignment (ADA). This method aligns a generative model of atomic positions -- initially trained on a simulated Boltzmann distribution -- with the distribution of experimental observations. We prove that our method recovers the target observable distribution, even with multiple, potentially correlated observables. We also empirically validate our framework on synthetic, molecular, and experimental protein data, demonstrating that it can align generative models with diverse observables. Our code is available at this https URL.
Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Biomolecules (q-bio.BM)
arXiv:2604.01169 [cs.LG]
(or arXiv:2604.01169v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.01169 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 291 words