Friction stir based welding, processing, extrusion and additive manufacturing
Friction stir welding and processing enabled the creation of stronger joints, novel ultrafine-
grained metals, new metal matrix composites, and multifunctional surfaces at user-defined …
grained metals, new metal matrix composites, and multifunctional surfaces at user-defined …
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
[LIVRE][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
[HTML][HTML] POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to
overcome common limitations shared by conventional reduced order models (ROMs)–built …
overcome common limitations shared by conventional reduced order models (ROMs)–built …
Multipole graph neural operator for parametric partial differential equations
One of the main challenges in using deep learning-based methods for simulating physical
systems and solving partial differential equations (PDEs) is formulating physics-based data …
systems and solving partial differential equations (PDEs) is formulating physics-based data …
Model reduction and neural networks for parametric PDEs
We develop a general framework for data-driven approximation of input-output maps
between infinitedimensional spaces. The proposed approach is motivated by the recent …
between infinitedimensional spaces. The proposed approach is motivated by the recent …
Poseidon: Efficient foundation models for pdes
We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is
based on a multiscale operator transformer, with time-conditioned layer norms that enable …
based on a multiscale operator transformer, with time-conditioned layer norms that enable …
Can physics-informed neural networks beat the finite element method?
Partial differential equations play a fundamental role in the mathematical modelling of many
processes and systems in physical, biological and other sciences. To simulate such …
processes and systems in physical, biological and other sciences. To simulate such …
Non-intrusive reduced order modeling of nonlinear problems using neural networks
We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial
differential equations (PDEs). The method extracts a reduced basis from a collection of high …
differential equations (PDEs). The method extracts a reduced basis from a collection of high …
[HTML][HTML] Physics-informed machine learning for reduced-order modeling of nonlinear problems
A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …
developed for efficient reduced-order modeling of parametrized partial differential equations …