Towards a population-informed approach to the definition of data-driven
models for structural dynamics
- URL: http://arxiv.org/abs/2307.09862v1
- Date: Wed, 19 Jul 2023 09:45:41 GMT
- Title: Towards a population-informed approach to the definition of data-driven
models for structural dynamics
- Authors: G. Tsialiamanis, N. Dervilis, D.J. Wagg, K. Worden
- Abstract summary: A population-based scheme is followed here and two different machine-learning algorithms from the meta-learning domain are used.
The algorithms seem to perform as intended and outperform a traditional machine-learning algorithm at approximating the quantities of interest.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Machine learning has affected the way in which many phenomena for various
domains are modelled, one of these domains being that of structural dynamics.
However, because machine-learning algorithms are problem-specific, they often
fail to perform efficiently in cases of data scarcity. To deal with such
issues, combination of physics-based approaches and machine learning algorithms
have been developed. Although such methods are effective, they also require the
analyser's understanding of the underlying physics of the problem. The current
work is aimed at motivating the use of models which learn such relationships
from a population of phenomena, whose underlying physics are similar. The
development of such models is motivated by the way that physics-based models,
and more specifically finite element models, work. Such models are considered
transferrable, explainable and trustworthy, attributes which are not trivially
imposed or achieved for machine-learning models. For this reason,
machine-learning approaches are less trusted by industry and often considered
more difficult to form validated models. To achieve such data-driven models, a
population-based scheme is followed here and two different machine-learning
algorithms from the meta-learning domain are used. The two algorithms are the
model-agnostic meta-learning (MAML) algorithm and the conditional neural
processes (CNP) model. The algorithms seem to perform as intended and
outperform a traditional machine-learning algorithm at approximating the
quantities of interest. Moreover, they exhibit behaviour similar to traditional
machine learning algorithms (e.g. neural networks or Gaussian processes),
concerning their performance as a function of the available structures in the
training population.
Related papers
- Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control [2.058941610795796]
This paper introduces a streamlined method for learning low-dimensional, physics-based models.
We validate our approach through simulations with various planar soft manipulators.
Thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
arXiv Detail & Related papers (2024-10-31T18:37:22Z) - Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey [19.70499936572449]
High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power.
Due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally.
This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology.
arXiv Detail & Related papers (2023-10-23T12:15:23Z) - A Meta-Learning Approach to Population-Based Modelling of Structures [0.0]
A major problem of machine-learning approaches in structural dynamics is the frequent lack of structural data.
Inspired by the recently-emerging field of population-based structural health monitoring, this work attempts to create models that are able to transfer knowledge within populations of structures.
The models trained using meta-learning approaches, are able to outperform conventional machine learning methods regarding inference about structures of the population.
arXiv Detail & Related papers (2023-02-15T23:01:59Z) - Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models.
The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
arXiv Detail & Related papers (2022-09-05T16:48:34Z) - Learning continuous models for continuous physics [94.42705784823997]
We develop a test based on numerical analysis theory to validate machine learning models for science and engineering applications.
Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.
arXiv Detail & Related papers (2022-02-17T07:56:46Z) - Which priors matter? Benchmarking models for learning latent dynamics [70.88999063639146]
Several methods have proposed to integrate priors from classical mechanics into machine learning models.
We take a sober look at the current capabilities of these models.
We find that the use of continuous and time-reversible dynamics benefits models of all classes.
arXiv Detail & Related papers (2021-11-09T23:48:21Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
There is an increasing need for active learning algorithms that are compatible with deep neural networks.
This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
arXiv Detail & Related papers (2021-06-17T17:26:31Z) - Model-Based Deep Learning [155.063817656602]
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques.
Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance.
We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
arXiv Detail & Related papers (2020-12-15T16:29:49Z) - Modeling System Dynamics with Physics-Informed Neural Networks Based on
Lagrangian Mechanics [3.214927790437842]
Two main modeling approaches often fail to meet requirements: first principles methods suffer from high bias, whereas data-driven modeling tends to have high variance.
We present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems.
Our findings are of interest for model-based control and system identification of mechanical systems.
arXiv Detail & Related papers (2020-05-29T15:10:43Z)
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.