Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy

• URL: http://arxiv.org/abs/2204.06764v1
• Date: Thu, 14 Apr 2022 05:44:59 GMT
• Title: Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy
• Abstract summary: supplemental physics-based features are introduced into a deep neural network (DNN) to improve predictive models for STEM applications. These additional features are injected into various single and multiple layers, and trained with four different dataset sizes. When physics-enhanced data is injected into multiple layers, reductions in error from 33.2% to 19.6%, 34.9% to 19.9%, 35.8% to 22.4%, and 43.0% to 28.4% are achieved for training dataset sizes of 261, 117, 60, and 30, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To improve predictive models for STEM applications, supplemental physics-based features computed from input parameters are introduced into single and multiple layers of a deep neural network (DNN). While many studies focus on informing DNNs with physics through differential equations or numerical simulation, much may be gained through integration of simplified relationships. To evaluate this hypothesis, a number of thin rectangular plates simply-supported on all edges are simulated for five materials. With plate dimensions and material properties as input features and fundamental natural frequency as the sole output, predictive performance of a purely data-driven DNN-based model is compared with models using additional inputs computed from simplified physical relationships among baseline parameters, namely plate weight, modulus of rigidity, and shear modulus. To better understand the benefit to model accuracy, these additional features are injected into various single and multiple DNN layers, and trained with four different dataset sizes. When these physics-enhanced models are evaluated against independent data of the same materials and similar dimensions to the training sets, supplementation with simplified physics-based parameters provides little reduction in prediction error over the baseline for models trained with dataset sizes of 60 and greater, although small improvement from 19.3% to 16.1% occurs when trained with a sparse size of 30. Conversely, notable accuracy gains occur when the independent test data is of material and dimensions not conforming to the training set. Specifically, when physics-enhanced data is injected into multiple DNN layers, reductions in error from 33.2% to 19.6%, 34.9% to 19.9%, 35.8% to 22.4%, and 43.0% to 28.4% are achieved for training dataset sizes of 261, 117, 60, and 30, respectively, demonstrating attainment of a degree of generalizability.

Related papers

• Inclusive electron-nucleus cross section models from domain adaptation [0.0]
  We apply transfer learning to construct data-driven models of electron-nucleus cross sections.<n>We fine-tune the models separately for (3)He, (6)Li, (16)O, (27)Al, (40)Ca, and (56)Fe.
  arXiv  Detail & Related papers  (2026-09-08T09:00:50Z)
• Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training [51.41246396610475]
  This paper aims to predict performance in closed-book question answering (QA) without the help of external tools.<n>We conduct large-scale retrieval and semantic analysis across the pre-training corpora of 21 publicly available and 3 custom-trained large language models.<n>Building on these foundations, we propose Size-dependent Mutual Information (SMI), an information-theoretic metric that linearly correlates pre-training data characteristics.
  arXiv  Detail & Related papers  (2025-02-06T13:23:53Z)
• Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties [10.955525128731654]
  Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. We present a physics-based training pipeline that tackles the pathology of data scarcity.
  arXiv  Detail & Related papers  (2024-07-03T02:57:40Z)
• Just How Flexible are Neural Networks in Practice? [89.80474583606242]
  It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters. In practice, however, we only find solutions via our training procedure, including the gradient and regularizers, limiting flexibility.
  arXiv  Detail & Related papers  (2024-06-17T12:24:45Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials [3.7655047338409893]
  ChargE3Net is an E(3)-equivariant graph neural network for predicting electron density in atomic systems. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials.
  arXiv  Detail & Related papers  (2023-12-08T21:56:19Z)
• Reusing Pretrained Models by Multi-linear Operators for Efficient Training [65.64075958382034]
  Training large models from scratch usually costs a substantial amount of resources. Recent studies such as bert2BERT and LiGO have reused small pretrained models to initialize a large model. We propose a method that linearly correlates each weight of the target model to all the weights of the pretrained model.
  arXiv  Detail & Related papers  (2023-10-16T06:16:47Z)
• Deep learning for full-field ultrasonic characterization [7.120879473925905]
  This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform. Two logics, namely the direct inversion and physics-informed neural networks (PINNs), are explored.
  arXiv  Detail & Related papers  (2023-01-06T05:01:05Z)
• Human Trajectory Prediction via Neural Social Physics [63.62824628085961]
  Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. We propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters.
  arXiv  Detail & Related papers  (2022-07-21T12:11:18Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT [0.0]
  The framework is generic in nature and can be applied to model a phenomenon from other fields of research too as long as its behaviour is known. A semi-supervised physics guided neural network (SPGNN) has been developed that predicts I-V characteristics of a gallium nitride-based high electron mobility transistor (GaN HEMT) The SPGNN significantly reduces the requirement of the training data by more than 80% for achieving similar or better performance than a traditional neural network (TNN) even for unseen conditions.
  arXiv  Detail & Related papers  (2021-10-20T18:48:50Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)
• Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval [14.131127382785973]
  The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP) Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. We show how features extracted from the IASI data by a CNN trained to predict a physical variable can be used as inputs to another statistical method designed to predict a different physical variable at low altitude.
  arXiv  Detail & Related papers  (2020-12-09T09:28:42Z)
• Large-scale Neural Solvers for Partial Differential Equations [48.7576911714538]
  Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs. Recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. We examine the applicability of continuous, mesh-free neural solvers for partial differential equations, physics-informed neural networks (PINNs) We discuss the accuracy of GatedPINN with respect to analytical solutions -- as well as state-of-the-art numerical solvers, such as spectral solvers.
  arXiv  Detail & Related papers  (2020-09-08T13:26:51Z)

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.