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A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks [article]

Hao Ma and Xiangyu Hu and Yuxuan Zhang and Nils Thuerey and Oskar J. Haidn
2020 arXiv   pre-print
Choosing heat conduction problem as an example, we compared the data- and physics-driven learning process with deep Convolutional Neural Networks (CNN).  ...  With several advantages and as an alternative to predict physics field, machine learning methods can be classified into two distinct types: data-driven relying on training data and physics-driven using  ...  Choosing the steady solution of heat conduction as an example, we first consider the original data-driven and physics-driven methods based on a deep CNN respectively and compare their learning progresses  ... 
arXiv:2005.08119v1 fatcat:fxidpozgfvfbhlcwltysicxcne

Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer based on Convolutional Neural Networks: Heat Conduction [article]

Jiang-Zhou Peng, Xianglei Liu, Nadine Aubry, Zhihua Chen, Wei-Tao Wu
2020 arXiv   pre-print
In current work, we develop a data-driven model for extremely fast prediction of steady-state heat conduction of a hot object with arbitrary geometry in a two-dimensional space.  ...  After training, the data-driven network model is able to accurately predict steady-state heat conduction of hot objects with complex geometries which has never been seen by the network model; and the prediction  ...  Methods In this paper, we propose a data-driven reduced order model based on deep convolutional neural networks (CNNs).  ... 
arXiv:2010.03854v1 fatcat:mfr7a74j7fgtdc63i7snehdvam

Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data [article]

Xiaoyu Zhao, Zhiqiang Gong, Yunyang Zhang, Wen Yao, Xiaoqian Chen
2021 arXiv   pre-print
To address this issue, this paper develops a physics-informed convolutional neural network (CNN) for the thermal simulation surrogate.  ...  The experiments demonstrate that the proposed method can provide comparable predictions with numerical method and data-driven deep learning models.  ...  , physics-informed Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data A PREPRINT 0.15 0.20 method physics-informed supervised (500  ... 
arXiv:2109.12482v1 fatcat:had4ufwmrnd4xbr6dr65u73a6m

HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems [article]

Yun Long, Xueyuan She, Saibal Mukhopadhyay
2019 arXiv   pre-print
A data-driven deep neural network (DNN) with Convolutional LSTM (ConvLSTM) as the backbone is employed to predict the time-varying evolution of the external forces/perturbations.  ...  In this paper, we present HybridNet, a framework that integrates data-driven deep learning and model-driven computation to reliably predict spatiotemporal evolution of a dynamical systems even with in-exact  ...  HybridNet consists of two interacting parts: First, at the front-end, Convolutional LSTM (ConvLSTM) [6] is used as the data-driven deep learning algorithm.  ... 
arXiv:1806.07439v2 fatcat:c6kipvfuejas3p3uiwjmqju3la

An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient [article]

Haiyang He, Jay Pathak
2020 arXiv   pre-print
Data driven methods are data hungry, to address this, Physics Informed Neural Networks (PINN) have been proposed.  ...  Therefore, this paper investigates an unsupervised learning approach for solving heat transfer equations on chip without using solution data and generalizing the trained network for predicting solutions  ...  For supervised approach, a data-driven framework for learning unknown time-dependent autonomous PDEs using deep neural networks is presented in [9] .  ... 
arXiv:2007.09684v1 fatcat:ktxcdsuv5ngedk6pkgqamlnicu

Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations [article]

Shen Wang, Mehdi Nikfar, Joshua C. Agar, Yaling Liu
2021 arXiv   pre-print
We achieve state-of-the-art results without any labeled simulation data, but using a custom data-driven and physics-informed loss function by using and small-scale solutions to prime the model to solve  ...  To improve computational efficiency, machine learning techniques have been used to create accelerated data-driven approximations for CFD.  ...  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput.  ... 
arXiv:2112.06419v1 fatcat:hd4caoonive5dokqz7x2cqqq7u

A Physics-Data-Driven Bayesian Method for Heat Conduction Problems [article]

Xinchao Jiang, Hu Wang, Yu li
2021 arXiv   pre-print
In this study, a novel physics-data-driven Bayesian method named Heat Conduction Equation assisted Bayesian Neural Network (HCE-BNN) is proposed.  ...  Compared with the existed pure data driven method, to acquire physical consistency and better performance of the data-driven model, the heat conduction equation is embedded into the loss function of the  ...  We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with this work.  ... 
arXiv:2109.00996v1 fatcat:iwav5e7p6fcohazvbkc2ana6oe

DiffusionNet: Accelerating the solution of Time-Dependent partial differential equations using deep learning [article]

Mahmoud Asem
2020 arXiv   pre-print
We demonstrate our model flexibility by applying our model with the same network architecture used to solve the transient heat conduction to solve the Inviscid Burgers equation and Steady-state heat conduction  ...  The model is trained on solution data calculated using the Alternating direction implicit method.  ...  [5] Used conditional generative adversarial networks (cGAN) to generate the solution of steady-state heat conduction problem and steady-state fluid flow in a two-dimensional domain problem via a data-driven  ... 
arXiv:2011.10015v1 fatcat:52gblihuxba3hjipja63ekocwu

PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain [article]

Han Gao, Luning Sun, Jian-Xun Wang
2020 arXiv   pre-print
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a  ...  The proposed method has been assessed by solving a number of PDEs on irregular domains, including heat equations and steady Navier-Stokes equations with parameterized boundary conditions and varying geometries  ...  Acknowledgment The authors would like to acknowledge the funds from National Science Foundation (NSF contract CMMI-1934300) and startup funds from the College of Engineering at University of Notre Dame  ... 
arXiv:2004.13145v2 fatcat:yopnmvhjsvdflmmv7ycggbhx5m

Deep Learning the Physics of Transport Phenomena [article]

Amir Barati Farimani, Joseph Gomes, Vijay S. Pande
2017 arXiv   pre-print
We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning.  ...  Using conditional generative adversarial networks (cGAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow purely on observation without  ...  We demonstrated successful learning and prediction for steady state heat conduction and incompressible fluid flow, two popular and widely applied physical phenomena.  ... 
arXiv:1709.02432v1 fatcat:l4za4vwztjhkley2xqu3muedua

Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks [article]

Yang Liu, Rui Hu, Adam Kraus, Prasanna Balaprakash, Aleksandr Obabko
2021 arXiv   pre-print
A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial-temporal CFD transient  ...  The neural network model was trained and optimized on a loss-of-flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient.  ...  Acknowledgement This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science,  ... 
arXiv:2109.04423v2 fatcat:kkwi57vfnneypempm4mwpkivvq

Deep learning predicts boiling heat transfer

Youngjoon Suh, Ramin Bostanabad, Yoonjin Won
2021 Scientific Reports  
The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features.  ...  Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves.  ...  Data availability The authors declare that all boiling data and codes supporting this study are available from the corresponding author upon reasonable request.  ... 
doi:10.1038/s41598-021-85150-4 pmid:33692489 pmcid:PMC7970936 fatcat:4kza3lp2tjeyzjycwpkncdp5gm

Data-driven inverse modelling through neural network (deep learning) and computational heat transfer

Hamid Reza Tamaddon-Jahromi, Neeraj Kavan Chakshu, Igor Sazonov, Llion M. Evans, Hywel Thomas, Perumal Nithiarasu
2020 Computer Methods in Applied Mechanics and Engineering  
The proposed method is tested for the linear/non-linear heat conduction, convection-conduction, and natural convection problems in which the boundary conditions are determined by providing three, four,  ...  In this work, the potential of carrying out inverse problems with linear and non-linear behaviour is investigated using deep learning methods.  ...  [21] proposed a general approximation model for real-time prediction of non-uniform steady laminar flow in a 2D or 3D domain based on Convolutional Neural Networks (CNNs) [22] .  ... 
doi:10.1016/j.cma.2020.113217 fatcat:47igd5oyyrcgrf2asjdk7yamue

DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks [article]

Mateus Dias Ribeiro and Abdul Rehman and Sheraz Ahmed and Andreas Dengel
2021 arXiv   pre-print
Therefore, we propose DeepCFD: a convolutional neural network (CNN) based model that efficiently approximates solutions for the problem of non-uniform steady laminar flows.  ...  The proposed model is able to learn complete solutions of the Navier-Stokes equations, for both velocity and pressure fields, directly from ground-truth data generated using a state-of-the-art CFD code  ...  Convolutional Neural Networks Convolutional Neural Networks (CNNs) have proven great capability of learning important features from images at the pixel level in order to make useful predictions for both  ... 
arXiv:2004.08826v3 fatcat:5jwpasi2vzewjkiq4xxvph5bgm

Lasers that learn: The interface of laser machining and machine learning

Benjamin Mills, James A. Grant‐Jacob
2021 IET Optoelectronics  
Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry.  ...  However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those  ...  Robert Eason for constructive help in reviewing this manuscript.  ... 
doi:10.1049/ote2.12039 fatcat:loc2t2l6end63k7sbpeubvys5u
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