56 citations · 118 across the 14 of their papers we have counts for
15 papers
Learning biophysical models of gene regulation with probability flow matching
Suryanarayana Maddu, Victor Chardès, Michael J. Shelley
Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellu…
Active Liquid Crystal Theory Explains the Collective Organization of Microtubules in Human Mitotic Spindles
Colm P. Kelleher, Suryanarayana Maddu, Mustafa Basaran +3
How thousands of microtubules and molecular motors self-organize into spindles remains poorly understood. By combining static, nanometer-resolution, large-scale electron tomography…
Inferring stochastic dynamics with growth from cross-sectional data
Stephen Zhang, Suryanarayana Maddu, Xiaojie Qiu +1
Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
Inferring biological processes with intrinsic noise from cross-sectional data
Suryanarayana Maddu, Victor Chardès, Michael. J. Shelley
Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explo…
Stochastic force inference via density estimation
Victor Chardès, Suryanarayana Maddu, Michael J. Shelley
Inferring dynamical models from low-resolution temporal data continues to be a significant challenge in biophysics, especially within transcriptomics, where separating molecular pr…
Learning fast, accurate, and stable closures of a kinetic theory of an active fluid
Suryanarayana Maddu, Scott Weady, Michael J. Shelley
Important classes of active matter systems can be modeled using kinetic theories. However, kinetic theories can be high dimensional and challenging to simulate. Reduced-order repre…
Learning locally dominant force balances in active particle systems
Dominik Sturm, Suryanarayana Maddu, Ivo F. Sbalzarini
We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in s…
Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
Sarah Perez, Suryanarayana Maddu, Ivo F. Sbalzarini +1
In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible…
Learning deterministic hydrodynamic equations from stochastic active particle dynamics
Suryanarayana Maddu, Quentin Vagne, Ivo F. Sbalzarini
We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our meth…
Parallel Discrete Convolutions on Adaptive Particle Representations of Images
Joel Jonsson, Bevan L. Cheeseman, Suryanarayana Maddu +2
We present data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer a…
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks
Suryanarayana Maddu, Dominik Sturm, Christian L. Müller +1
We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Ne…
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman +2
Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretiz…
Learning physically consistent mathematical models from data using group sparsity
Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller +1
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetr…
Stability selection enables robust learning of partial differential equations from limited noisy data
Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini +1
We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approac…