Numerical solvers of partial differential equations (PDEs) have been widely employed for
simulating physical systems. However, the computational cost remains a major bottleneck in …

Enabling fast and accurate physical simulations with data has become an important area of
computational physics to aid in inverse problems, design-optimization, uncertainty …

This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …

Numerically solving partial differential equations (PDEs) can be challenging and
computationally expensive. This has led to the development of reduced-order models …

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions
with well-characterized propagation of uncertainties and resulting inferences for single-shot …

Recent work in data-driven modeling has demonstrated that a weak formulation of model
equations enhances the noise robustness of a wide range of computational methods. In this …

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification
(gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order …

While projection-based reduced order models can reduce the dimension of full order
solutions, the resulting reduced models may still contain terms that scale with the full order …

We propose a latent space dynamics identification method, namely tLaSDI, that embeds the
first and second principles of thermodynamics. The latent variables are learned through an …

Numerous cutting-edge scientific technologies originate at the laboratory scale, but
transitioning them to practical industry applications is a formidable challenge. Traditional …