Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning. - Peter Y. Lu; Samuel Kim; Marin Soljacic (CoRR, 2019)

Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. - Samuel Kim; Peter Y. Lu; Charlotte Loh; Jamie Smith; Jasper Snoek; Marin Soljacic (CoRR, 2021)

Discovering Sparse Interpretable Dynamics from Partial Observations. - Peter Y. Lu; Joan Ariño; Marin Soljacic (CoRR, 2021)

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery. - Samuel Kim; Peter Y. Lu; Srijon Mukherjee; Michael Gilbert; Li Jing 001; Vladimir Ceperic; Marin Soljacic (IEEE Trans. Neural Networks Learn. Syst., 2021)

Discovering Conservation Laws using Optimal Transport and Manifold Learning. - Peter Y. Lu; Rumen Dangovski; Marin Soljacic (CoRR, 2022)

Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. - Samuel Kim; Peter Y. Lu; Charlotte Loh; Jamie Smith; Jasper Snoek; Marin Soljacic (Trans. Mach. Learn. Res., 2022)

Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. - Owen M. Dugan; Peter Y. Lu; Rumen Dangovski; Di Luo; Marin Soljacic (ICML, 2023)

Training neural operators to preserve invariant measures of chaotic attractors. - Ruoxi Jiang; Peter Y. Lu; Elena Orlova; Rebecca Willett (NeurIPS, 2023)

Model Stitching: Looking For Functional Similarity Between Representations. - Adriano Hernandez; Rumen Dangovski; Peter Y. Lu; Marin Soljacic (CoRR, 2023)

Multimodal Learning for Crystalline Materials. - Viggo Moro; Charlotte Loh; Rumen Dangovski; Ali Ghorashi; Andrew Ma; Zhuo Chen; Peter Y. Lu; Thomas Christensen; Marin Soljacic (CoRR, 2023)

Deep Stochastic Mechanics. - Elena Orlova; Aleksei Ustimenko; Ruoxi Jiang; Peter Y. Lu; Rebecca Willett (ICML, 2024)

Embed and Emulate: Contrastive representations for simulation-based inference. - Ruoxi Jiang; Peter Y. Lu; Rebecca Willett (CoRR, 2024)

Deep Learning and Symbolic Regression for Discovering Parametric Equations. - Michael Zhang; Samuel Kim; Peter Y. Lu; Marin Soljacic (IEEE Trans. Neural Networks Learn. Syst., 2024)

Hierarchical Implicit Neural Emulators. - Ruoxi Jiang; Xiao Zhang; Karan Jakhar; Peter Y. Lu; Pedram Hassanzadeh; Michael Maire; Rebecca Willett (CoRR, 2025)