Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches

• URL: http://arxiv.org/abs/2205.05928v1
• Date: Thu, 12 May 2022 07:40:35 GMT
• Title: Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches
• Authors: Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi
• Abstract summary: We apply deep learning techniques to generate accurate, efficient and real-time reduced order models. We extensively test the reliability of the proposed procedures on micromirrors, arches and gyroscopes. By addressing an electromechanical gyroscope, we show that the non-intrusive deep learning approach generalizes easily to complex multiphysics problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient and real-time reduced order models to be used as virtual twin for the simulation and optimization of higher-level complex systems. We extensively test the reliability of the proposed procedures on micromirrors, arches and gyroscopes, also displaying intricate dynamical evolutions like internal resonances. In particular, we discuss the accuracy of the deep learning technique and its ability to replicate and converge to the invariant manifolds predicted using the recently developed direct parametrization approach that allows extracting the nonlinear normal modes of large finite element models. Finally, by addressing an electromechanical gyroscope, we show that the non-intrusive deep learning approach generalizes easily to complex multiphysics problems

Related papers

• Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddings [3.825457221275617]
  We introduce a data-driven computational framework to derive low-dimensional linear models for nonlinear dynamical systems. This framework enables global stability analysis through interpretable linear models that capture the underlying system structure. Our method offers a promising pathway to analyze complex dynamical behaviors across fields such as physics, climate science, and engineering.
  arXiv  Detail & Related papers  (2024-11-01T19:27:47Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Efficient quantum simulation of nonlinear interactions using SNAP and Rabi gates [0.7366405857677227]
  We present a deterministic simulation technique that efficiently models nonlinear bosonic dynamics. Our proposed simulation method facilitates high-fidelity modeling of phenomena that emerge from higher-order bosonic interactions.
  arXiv  Detail & Related papers  (2023-12-15T16:44:43Z)
• Learning minimal representations of stochastic processes with variational autoencoders [52.99137594502433]
  We introduce an unsupervised machine learning approach to determine the minimal set of parameters required to describe a process. Our approach enables for the autonomous discovery of unknown parameters describing processes.
  arXiv  Detail & Related papers  (2023-07-21T14:25:06Z)
• Robust Hamiltonian Engineering for Interacting Qudit Systems [50.591267188664666]
  We develop a formalism for the robust dynamical decoupling and Hamiltonian engineering of strongly interacting qudit systems. We experimentally demonstrate these techniques in a strongly-interacting, disordered ensemble of spin-1 nitrogen-vacancy centers.
  arXiv  Detail & Related papers  (2023-05-16T19:12:41Z)
• Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics [77.34726150561087]
  Recent developments in artificial neural networks, particularly deep learning (DL), are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics.
  arXiv  Detail & Related papers  (2022-12-18T02:03:00Z)
• On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver [59.13397937903832]
  We introduce a deep learning-based corrector called Neural Vector (NeurVec) NeurVec can compensate for integration errors and enable larger time step sizes in simulations. Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
  arXiv  Detail & Related papers  (2022-08-07T09:02:18Z)
• Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks [0.0]
  We introduce MultiScaleGNN, a novel multi-scale graph neural network model for learning to infer unsteady continuum mechanics. We demonstrate this method on advection problems and incompressible fluid dynamics, both fundamental phenomena in oceanic and atmospheric processes. Simulations obtained with MultiScaleGNN are between two and four orders of magnitude faster than those on which it was trained.
  arXiv  Detail & Related papers  (2022-05-05T13:33:03Z)
• Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns [48.53244254413104]
  We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images. We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures. Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
  arXiv  Detail & Related papers  (2022-02-01T03:53:39Z)
• Using scientific machine learning for experimental bifurcation analysis of dynamic systems [2.204918347869259]
  This study focuses on training universal differential equation (UDE) models for physical nonlinear dynamical systems with limit cycles. We consider examples where training data is generated by numerical simulations, whereas we also employ the proposed modelling concept to physical experiments. We use both neural networks and Gaussian processes as universal approximators alongside the mechanistic models to give a critical assessment of the accuracy and robustness of the UDE modelling approach.
  arXiv  Detail & Related papers  (2021-10-22T15:43:03Z)
• Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows [0.0]
  We develop continuous spin-glass distributions with normalizing flows to model correlations in generic discrete problems. We demonstrate that key physical and computational properties of the spin-glass phase can be successfully learned. Remarkably, we observe that the learning itself corresponds to a spin-glass phase transition within the layers of the trained normalizing flows.
  arXiv  Detail & Related papers  (2020-01-02T19:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.