2 citations · 4 across the 11 of their papers we have counts for
16 papers
Hybrid coupling with numerics-informed neural networks and the overlapping Schwarz alternating method
George Chumbipuma, Irina Tezaur, Alejandro Diaz +1
We develop a hybrid modeling framework for coupling pre-trained numerics-informed neural networks (NINNs) with classical full order models (FOMs) using the overlapping Schwarz alte…
Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning
Trishit Mondal, Irina Tezaur, Anthony Gruber
Hybrid domain decomposition methods provide a flexible framework for coupling full order models (FOMs) and reduced order models (ROMs), but typically assume the model assigned to e…
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Sidharth S. Menon, Irina Tezaur, Ameya D. Jagtap
Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability while solving partial differen…
Learning Long-Term Stable Operator Inference Reduced-Order Models of Fluid Flows through Online Spatial Filtering
Ian Moore, Ping-Hsuan Tsai, Anthony Gruber +4
This paper introduces an online evolve--filter--relax (EFR) strategy for long-term stability of Operator Inference (OpInf) reduced-order models (ROMs) of complex fluid flow simulat…
Flexible and Stable Dynamics Discovery with Onsager's Variational Principle
Anthony Gruber, Ritoban Roy-Chowdhury, Irina Tezaur +1
Variational principles specify the dynamics of a physical system via the extremization of associated functional data. Onsager's variational principle (OVP), which characterizes dis…
On the role of relaxation and acceleration in the non-overlapping Schwarz alternating method for coupling
Giulia Sambataro, Irina Tezaur
The purpose of this paper is to study the influence of relaxation and acceleration techniques on the convergence behavior of the non-overlapping Schwarz algorithm with alternating…
NN-OpInf: an operator inference approach using structure-preserving composable neural networks
Eric Parish, Anthony Gruber, Patrick Blonigan +1
We propose neural network operator inference (NN-OpInf): a structure-preserving, composable, and minimally restrictive operator inference framework for the non-intrusive reduced-or…
Hybrid coupling with operator inference and the overlapping Schwarz alternating method
Irina Tezaur, Eric Parish, Anthony Gruber +3
This paper presents a novel hybrid approach for coupling subdomain-local non-intrusive Operator Inference (OpInf) reduced order models (ROMs) with each other and with subdomain-loc…
Domain Decomposition-Based Coupling of High-Fidelity Finite Element and Reduced Order Operator Inference Models Using the Schwarz Alternating Method
Ian Moore, Anthony Gruber, Chris Wentland +1
We propose a novel hybrid domain decomposition method that couples sub-domain-local high-fidelity finite element (FE) models with reduced order models (ROMs) using the Schwarz alte…
Transmission Conditions for the Non-Overlapping Schwarz Coupling of Full Order and Operator Inference Models
Cameron Rodriguez, Irina Tezaur, Alejandro Mota +3
This work investigates transmission conditions for the domain decomposition-based coupling of subdomain-local models using the non-overlapping Schwarz alternating method (NO-SAM).…
Domain Decomposition-based coupling of Operator Inference reduced order models via the Schwarz alternating method
Ian Moore, Christopher Wentland, Anthony Gruber +1
This paper presents and evaluates an approach for coupling together subdomain-local reduced order models (ROMs) constructed via non-intrusive operator inference (OpInf) with each o…
The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models
Christopher R. Wentland, Francesco Rizzi, Joshua Barnett +1
This paper presents and evaluates a framework for the coupling of subdomain-local projection-based reduced order models (PROMs) using the Schwarz alternating method following a dom…
Variationally consistent Hamiltonian model reduction
Anthony Gruber, Irina Tezaur
Though ubiquitous as first-principles models for conservative phenomena, Hamiltonian systems present numerous challenges for model reduction even in relatively simple, linear cases…
Domain decomposition-based coupling of physics-informed neural networks via the Schwarz alternating method
Will Snyder, Irina Tezaur, Christopher Wentland
Physics-informed neural networks (PINNs) are appealing data-driven tools for solving and inferring solutions to nonlinear partial differential equations (PDEs). Unlike traditional…
Canonical and Noncanonical Hamiltonian Operator Inference
Anthony Gruber, Irina Tezaur
A method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this…
Residual-based stabilized reduced-order models of the transient convection-diffusion-reaction equation obtained through discrete and continuous projection
Eric Parish, Masayuki Yano, Irina Tezaur +1
Galerkin and Petrov-Galerkin projection-based reduced-order models (ROMs) of transient partial differential equations are typically obtained by performing a dimension reduction and…