11 papers
AEGIS: Differentiable Mars Climate Model with Neural Closures
Sameera S Kashyap, Victor Cruz, Angel Yepez +1
General circulation models (GCMs) are the primary tool for simulating planetary atmospheres. They play a vital role in understanding Mars's atmosphere, as forecasting its unique we…
You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution
Prathamesh Pradeep Khole, Shreya Handa, Utkarsh Gupta +1
Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and pixelwise uncertainty, most of…
Logarithmic derivatives of variational and singular stochastic partial differential equations
Ehsan Mirafzali, Frank Proske, Razvan Marinescu
For a stochastic partial differential equation posed on a Gelfand triple and satisfying the fully local monotone conditions of Röckner, Shang and Zhang, we compute the logarithmic…
Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
Sanya Murdeshwar, Sanjit Shashi, Kevin Bachelor +3
Coarse-grained (CG) molecular dynamics enables simulations of atomic systems such as biomolecules at timescales inaccessible to all-atom (AA) methods, but existing CG neural potent…
Spinverse: Differentiable Physics for Permeability-Aware Microstructure Reconstruction from Diffusion MRI
Prathamesh Pradeep Khole, Mario M. Brenes, Zahra Kais Petiwala +5
Diffusion MRI (dMRI) is sensitive to microstructural barriers, yet most existing methods either assume impermeable boundaries or estimate voxel-level parameters without recovering…
Holographic generative flows with AdS/CFT
Ehsan Mirafzali, Sanjit Shashi, Sanya Murdeshwar +3
Holography, in the form of the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, offers a natural setting for generative modelling. Data on a boundary manifold lifts…
Latent Spaces for Langevin Dynamics
Andy Bruce, Alexander Aghili, Razvan Marinescu +1
In the field of machine learning coarse-grained potentials in molecular dynamics, many propagators require that the effective Hamiltonian is quadratic in momentum, thus limiting th…
TICA-Based Free Energy Matching for Machine-Learned Molecular Dynamics
Alexander Aghili, Andy Bruce, Daniel Sabo +1
Molecular dynamics (MD) simulations provide atomistic insight into biomolecular systems but are often limited by high computational costs required to access long timescales. Coarse…
Active Learning for Machine Learning Driven Molecular Dynamics
Kevin Bachelor, Sanya Murdeshwar, Daniel Sabo +1
Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom…
Score-Based Diffusion Models in Infinite Dimensions: A Malliavin Calculus Perspective
Ehsan Mirafzali, Frank Proske, Daniele Venturi +1
We study score-based diffusion modelling in infinite-dimensional separable Hilbert spaces through Malliavin calculus, extending the analysis of generative models beyond the finite-…
A Malliavin calculus approach to score functions in diffusion generative models
Ehsan Mirafzali, Frank Proske, Utkarsh Gupta +2
Score-based diffusion generative models have recently emerged as a powerful tool for modelling complex data distributions. These models aim at learning the score function, which de…