5 citations · 11 across the 5 of their papers we have counts for
7 papers
Normalizing Flow Maps
Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng +6
Scalable approaches to learning neural dynamical systems have placed diffusion, flow matching, and few-step generators in flow maps at the forefront of generative modeling. Such st…
Global Transport Couplings for Classifier-Free Guided Flows
Katarina Petrović, Zander W. Blasingame, Danyal Rehman +5
Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains unclear. A natural approach c…
FALCON: Few-step Accurate Likelihoods for Continuous Flows
Danyal Rehman, Tara Akhound-Sadegh, Artem Gazizov +2
Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann Generators tackle this problem by pairing a genera…
Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
Danyal Rehman, Oscar Davis, Jiarui Lu +5
Simulation-free training frameworks have been at the forefront of the generative modelling revolution in continuous spaces, leading to large-scale diffusion and flow matching model…
Attention-enhanced neural differential equations for physics-informed deep learning of ion transport
Danyal Rehman, John H. Lienhard
Species transport models typically combine partial differential equations (PDEs) with relations from hindered transport theory to quantify electromigrative, convective, and diffusi…
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Grégoire Mialon, Quentin Garrido, Hannah Lawrence +3
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering…
Physics-constrained neural differential equations for learning multi-ionic transport
Danyal Rehman, John H. Lienhard
Continuum models for ion transport through polyamide nanopores require solving partial differential equations (PDEs) through complex pore geometries. Resolving spatiotemporal featu…