BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
- URL: http://arxiv.org/abs/2307.14623v2
- Date: Fri, 25 Aug 2023 03:17:29 GMT
- Title: BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
- Authors: Sheikh Md Shakeel Hassan, Arthur Feeney, Akash Dhruv, Jihoon Kim,
Youngjoon Suh, Jaiyoung Ryu, Yoonjin Won, Aparna Chandramowlishwaran
- Abstract summary: This dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 79 simulations.
BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for machine learning (ML) research.
We showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics.
- Score: 15.681756756338546
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the field of phase change phenomena, the lack of accessible and diverse
datasets suitable for machine learning (ML) training poses a significant
challenge. Existing experimental datasets are often restricted, with limited
availability and sparse ground truth data, impeding our understanding of this
complex multiphysics phenomena. To bridge this gap, we present the BubbleML
Dataset
\footnote{\label{git_dataset}\url{https://github.com/HPCForge/BubbleML}} which
leverages physics-driven simulations to provide accurate ground truth
information for various boiling scenarios, encompassing nucleate pool boiling,
flow boiling, and sub-cooled boiling. This extensive dataset covers a wide
range of parameters, including varying gravity conditions, flow rates,
sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is
validated against experimental observations and trends, establishing it as an
invaluable resource for ML research. Furthermore, we showcase its potential to
facilitate exploration of diverse downstream tasks by introducing two
benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b)
operator networks for learning temperature dynamics. The BubbleML dataset and
its benchmarks serve as a catalyst for advancements in ML-driven research on
multiphysics phase change phenomena, enabling the development and comparison of
state-of-the-art techniques and models.
Related papers
- Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
This study focuses on the application of Multimodal Large Language Models (MLLMs) within the domain of autonomous driving.
We evaluate the capability of various MLLMs as world models for driving from the perspective of a fixed in-car camera.
Our results highlight a critical gap in the current capabilities of state-of-the-art MLLMs.
arXiv Detail & Related papers (2024-05-09T17:52:42Z) - Exploring the efficacy of a hybrid approach with modal decomposition over fully deep learning models for flow dynamics forecasting [2.8686437689115363]
We study the application of time series forecasting to fluid dynamics problems.
The aim is to predict the flow dynamics using only past information.
We focus our study on models based on deep learning that do not require a high amount of data for training.
arXiv Detail & Related papers (2024-04-27T12:43:02Z) - Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation.
In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model.
We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
arXiv Detail & Related papers (2024-04-11T09:23:36Z) - Accurate machine learning force fields via experimental and simulation
data fusion [0.0]
Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span scales of classical interatomic potentials at quantum-level accuracy.
Here we leverage both Density Functional Theory (DFT) calculations and experimentally measured mechanical properties and lattice parameters to train an ML potential of titanium.
We demonstrate that the fused data learning strategy can concurrently satisfy all target objectives, thus resulting in a molecular model of higher accuracy compared to the models trained with a single source data.
arXiv Detail & Related papers (2023-08-17T18:22:19Z) - Machine learning enabled experimental design and parameter estimation
for ultrafast spin dynamics [54.172707311728885]
We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED)
Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED.
Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
arXiv Detail & Related papers (2023-06-03T06:19:20Z) - Data-Efficient Learning via Minimizing Hyperspherical Energy [48.47217827782576]
This paper considers the problem of data-efficient learning from scratch using a small amount of representative data.
We propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL.
arXiv Detail & Related papers (2022-06-30T11:39:12Z) - Physics-informed machine learning with differentiable programming for
heterogeneous underground reservoir pressure management [64.17887333976593]
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection.
Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface.
We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
arXiv Detail & Related papers (2022-06-21T20:38:13Z) - Prediction of liquid fuel properties using machine learning models with
Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels.
Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach.
The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
arXiv Detail & Related papers (2021-10-18T14:43:50Z) - Inferring micro-bubble dynamics with physics-informed deep learning [0.0]
Multiphase flow simulation requires high accuracy due to possible component losses that may be caused by sparse meshing during the computation.
We propose a novel deep learning framework BubbleNet, which entails three main parts: deep neural networks (DNN) with sub nets for predicting different physics fields.
Results indicate our framework can predict the physics fields more accurately, estimating the absolute prediction errors.
arXiv Detail & Related papers (2021-05-15T09:17:56Z) - Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase
Flow Simulation Using Validation Data [5.099083753474628]
Feature-similarity measurement (FSM) is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach.
FSM can substantially improve the prediction of the coarse-mesh CFD model, regardless of the choice of interfacial closures.
arXiv Detail & Related papers (2020-05-07T21:25:22Z) - Using Deep Learning to Explore Local Physical Similarity for
Global-scale Bridging in Thermal-hydraulic Simulation [4.350727579753697]
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions.
This paper proposes a data-driven approach, Feature Similarity Measurement FFSM, to overcome these difficulties.
Deep learning is applied to construct and explore the relationship between the local physical features and simulation errors.
arXiv Detail & Related papers (2020-01-06T20:14:46Z)
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.