5 papers
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…
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…
From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
Sawan Kumar, Tapas Tripura, Rajdip Nayek +1
Operator learning offers a powerful paradigm for solving parametric partial differential equations (PDEs), but scaling probabilistic neural operators such as the recently proposed…
FUsion-based ConstitutivE model (FuCe): Towards model-data augmentation in constitutive modelling
Tushar, Sawan Kumar, Souvik Chakraborty
Constitutive modelling is crucial for engineering design and simulations to accurately describe material behavior. However, traditional phenomenological models often struggle to ca…