Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics

• URL: http://arxiv.org/abs/2004.02569v1
• Date: Mon, 6 Apr 2020 11:32:37 GMT
• Title: Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics
• Authors: Jussi M\"a\"att\"a, Viacheslav Bazaliy, Jyri Kimari, Flyura Djurabekova, Kai Nordlund, Teemu Roos
• Abstract summary: We propose a gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data.
• Score: 0.24792948967354234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms.

Related papers

• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Training Deep Surrogate Models with Large Scale Online Learning [48.7576911714538]
  Deep learning algorithms have emerged as a viable alternative for obtaining fast solutions for PDEs. Models are usually trained on synthetic data generated by solvers, stored on disk and read back for training. It proposes an open source online training framework for deep surrogate models.
  arXiv  Detail & Related papers  (2023-06-28T12:02:27Z)
• 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)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling [0.6445605125467573]
  Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database. This paper introduces deep learning techniques under the data-driven framework to address these fundamental issues in nonlinear materials modeling. The offline trained autoencoder and the discovered embedding space are then incorporated in the online data-driven computation.
  arXiv  Detail & Related papers  (2022-09-03T20:13:47Z)
• Calibrating constitutive models with full-field data via physics informed neural networks [0.0]
  We propose a physics-informed deep-learning framework for the discovery of model parameterizations given full-field displacement data. We work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. We demonstrate that informed machine learning is an enabling technology and may shift the paradigm of how full-field experimental data is utilized to calibrate models.
  arXiv  Detail & Related papers  (2022-03-30T18:07:44Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)
• A physics-informed operator regression framework for extracting data-driven continuum models [0.0]
  We present a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space. We demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows.
  arXiv  Detail & Related papers  (2020-09-25T01:13:51Z)
• On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case [0.0]
  Deep learning techniques are tested in the context of a classification problem encountered in the domain of high-energy physics. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. A new wrapper library for PyTorch called LUMIN is presented, which incorporates all of the techniques studied.
  arXiv  Detail & Related papers  (2020-02-03T12:29:59Z)

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.