216 citations · 379 across the 13 of their papers we have counts for
14 papers
Primal-Dual Flow Matching for Sample-Wise Constrained Generation
Zhengyan Huan, Peter Y. Lu, Shuchin Aeron
We consider the problem of learning to generate data while satisfying a prescribed \emph{sample-wise} constraint with a user-specified probability of constraint satisfaction. Start…
Flow-based Phase-space Tomography of Continuous-variable Quantum States
Owen Dugan, Rumen Dangovski, Peter Y. Lu +1
Continuous-variable quantum state tomography is limited by the cost of resolving non-Gaussian structure in high-dimensional phase space. We introduce QST-Flow, a quantum state tomo…
Multimodal Foundation Models for Material Property Prediction and Discovery
Viggo Moro, Charlotte Loh, Rumen Dangovski +7
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recentl…
Training neural operators to preserve invariant measures of chaotic attractors
Ruoxi Jiang, Peter Y. Lu, Elena Orlova +1
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neura…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusio…
Model Stitching: Looking For Functional Similarity Between Representations
Adriano Hernandez, Rumen Dangovski, Peter Y. Lu +1
Model stitching (Lenc & Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be in…
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski +2
Studying the dynamics of open quantum systems can enable breakthroughs both in fundamental physics and applications to quantum engineering and quantum computation. Since the densit…
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Peter Y. Lu, Rumen Dangovski, Marin Soljačić
Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corres…
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu +1
Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery. However, symbolic re…
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh +3
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-b…
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Samuel Kim, Peter Y. Lu, Srijon Mukherjee +4
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the trai…
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Peter Y. Lu, Samuel Kim, Marin Soljačić
Experimental data is often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by th…
Collision Dynamics of Particle Clusters in a Two-dimensional Granular Gas
Justin C. Burton, Peter Y. Lu, Sidney R. Nagel
In a granular gas, inelastic collisions produce an instability in which the constituent particles cluster heterogeneously. These clusters then interact with each other, further dec…
Energy Loss at Propagating Jamming Fronts in Granular Gas Clusters
Justin C. Burton, Peter Y. Lu, Sidney R. Nagel
We explore the initial moments of impact between two dense granular clusters in a two-dimensional geometry. The particles are composed of solid CO and are levitated on a hot…