# A strategy with reduced models dedicated to parametrized nonlinear strongly coupled thermo-poroelasticity problems

## TL;DR

This paper introduces a reduced-order modeling approach using the LATIN-PGD method for efficiently solving parametrized nonlinear strongly coupled thermo-poroelasticity problems, validated on benchmarks and industrial scenarios.

## Contribution

It extends PGD-based reduced models to handle complex coupled thermo-poroelasticity problems with parameter variability and nonlinearities, improving computational efficiency.

## Key findings

- The method performs well on standard benchmarks.
- It significantly reduces computation time compared to naive approaches.
- Effective for both academic and industrial applications.

## Abstract

This paper offers an approach to deal with parametrized nonlinear strongly coupled thermo-poroelasticity problems. The approach uses the LATIN-PGD method and extends previous work in multiphysics problems. Proper Generalized Decomposition (PGD) allows the building of independent reduced-order bases for each physics. This point is particularly appropriate for thermo-poroelasticity problems whose physics present different dynamics. In parametrized problems dealing with material variability, a new computation is initialized with the result of a previous simulation to speed up the computation times. As a first step, the solver is validated on a standard benchmark in thermo-poroelasticity. The solver shows good performance even in the nonlinear frame. Then, the approach for parametrized problems is addressed on an academic problem and a more complex one, which is part of an industrial process. The results show that the method is effective and less time-consuming than naive approaches.