2 citations · 3 across the 8 of their papers we have counts for
8 papers
GeoQ: Geometry-Aware Conditional Quantile Error Estimation for Scientific Surrogate Models
Khoa Nguyen, Daniel Serino, Aviral Prakash +1
Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive settings requires input-depende…
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws
Aviral Prakash, Marc L. Klasky
Reduced-order models, such as latent dynamics models, are becoming mainstream for accelerating simulations for parameterized physical systems governed by nonlinear conservation law…
Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
Samantha Friess, Aviral Prakash, John A. Evans
Neural networks offer highly expressive turbulence closures, yet their complexity obscures the physical mechanisms they aim to model, and their computational cost can limit their t…
ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized partial differential equations from sparse and noisy data
Aviral Prakash, Ben S. Southworth, Marc L. Klasky
Multi-query applications such as parameter estimation, uncertainty quantification and design optimization for parameterized PDE systems are expensive due to the high computational…
Nonintrusive projection-based reduced order modeling using stable learned differential operators
Aviral Prakash, Yongjie Jessica Zhang
Nonintrusive projection-based reduced order models (ROMs) are essential for dynamics prediction in multi-query applications where access to the source of the underlying full order…
SNF-ROM: Projection-based nonlinear reduced order modeling with smooth neural fields
Vedant Puri, Aviral Prakash, Levent Burak Kara +1
Reduced order modeling lowers the computational cost of solving PDEs by learning a low-order spatial representation from data and dynamically evolving these representations using m…
Data-driven identification of stable differential operators using constrained regression
Aviral Prakash, Yongjie Jessica Zhang
Identifying differential operators from data is essential for the mathematical modeling of complex physical and biological systems where massive datasets are available. These opera…
Projection-based reduced order modeling and data-driven artificial viscosity closures for incompressible fluid flows
Aviral Prakash, Yongjie Jessica Zhang
Projection-based reduced order models rely on offline-online model decomposition, where the data-based energetic spatial basis is used in the expensive offline stage to obtain equa…