1 citations · 1 across the 17 of their papers we have counts for
18 papers
Training-Loss Guarantees for Muon with Finite-Step Newton--Schulz Orthogonalization
Amartya Roy, Souvik Chakraborty
Existing convergence analyses of Muon either assume exact orthogonalization or analyze classical Newton--Schulz polynomials, and guarantee only stationarity, so it is unresolved wh…
Beyond Pairwise Graphs in Science: Hypergraph Adaptive Wavelet Operators for Parametric PDEs
Rajat Sarkar, Venkataramana Runkana, Souvik Chakraborty
Physical systems are often modeled by solution operators that map input fields, parameters, geometries, or past states to steady or future physical states. Learning these maps is d…
See, Hypothesize, Validate: Multimodal Agentic Framework for Discovering Governing PDEs
Sarang Manoj Pekhale, Amartya Roy, Rajat Sarkar +1
Discovering governing partial differential equations (PDEs) from observational data remains a core challenge across the sciences. Existing sparse-regression, symbolic-regression, a…
Towards Data-Efficient Cross-Device Generalization of Grad-Shafranov Equilibria via Transfer Learning Neural Operator
Jay Phil Yoo, William Howes, Yashika Ghai +3
Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion. However, Grad-…
Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator
William Howes, Jason Yoo, Kazuma Kobayashi +4
Real-time inference of inaccessible interior physical fields from sparse boundary observations is a fundamental but unresolved problem in scientific machine learning, with direct r…
Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems
Mridul Tiwari, Sawan Kumar, Md Rushdie Ibne Islam +1
Smoothed Particle Hydrodynamics (SPH_ is a mesh-free Lagrangian method renowned for modeling large deformations and free-surface flows, yet classical formulations remain confined t…
Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators
Samrendra Roy, Souvik Chakraborty, Syed Bahauddin Alam
Neural operators have emerged as fast surrogate models for physics simulations, yet they remain acutely vulnerable to adversarial perturbations, a critical liability for safety-cri…
SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
Samrendra Roy, Souvik Chakraborty, Rizwan-uddin +1
Neural operators have emerged as powerful surrogates for partial differential equation (PDE) solvers, yet they are typically trained as monolithic models for individual PDEs, requi…
Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks
Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage +9
Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as \texttt{MESA} (Modules for Experiments in Stell…
Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees
Sawan Kumar, Souvik Chakraborty
Modeling non-stationary processes, where statistical properties vary across the input domain, is a critical challenge in machine learning; yet most scalable methods rely on a simpl…
Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems
Akshay Thakur, Sawan Kumar, Matthew Zahr +1
Solving partial differential equations (PDEs) within the framework of probabilistic numerics offers a principled approach to quantifying epistemic uncertainty arising from discreti…
Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs
Subhankar Sarkar, Souvik Chakraborty
Solving partial differential equations (PDEs) efficiently and accurately remains a cornerstone challenge in science and engineering, especially for problems involving complex geome…
A Bayesian Approach for Discovering Time- Delayed Differential Equation from Data
Debangshu Chowdhury, Souvik Chakraborty
Time-delayed differential equations (TDDEs) are widely used to model complex dynamic systems where future states depend on past states with a delay. However, inferring the underlyi…
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
The growing demand for accurate, efficient, and scalable solutions in computational mechanics highlights the need for advanced operator learning algorithms that can efficiently han…
Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid
Subhankar Sarkar, Souvik Chakraborty
Scientific machine learning has seen significant progress with the emergence of operator learning. However, existing methods encounter difficulties when applied to problems on unst…
Discovering governing equation in structural dynamics from acceleration-only measurements
Calvin Alvares, Souvik Chakraborty
Over the past few years, equation discovery has gained popularity in different fields of science and engineering. However, existing equation discovery algorithms rely on the availa…
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared with traditional methods. How…
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
Akshay Thakur, Souvik Chakraborty
We propose a neural operator framework, termed mixture density nonlinear manifold decoder (MD-NOMAD), for stochastic simulators. Our approach leverages an amalgamation of the point…