Burgers Inverse
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Burgers Inverse
Developed by piotrnobis
A deep learning model for solving the inverse problem of Burgers equation, capable of predicting velocity evolution and estimating physical parameters
Downloads 335
Release Time : 4/14/2024
Model Overview
This model focuses on solving the inverse problem of Burgers equation through deep learning, predicting velocity evolution based on initial conditions, estimating hidden physical parameters, and handling incomplete data.
Model Features
Initial Condition Prediction
Predicts the development of liquid velocity over time based on initial conditions
Physical Parameter Estimation
Estimates hidden physical parameters such as viscosity coefficient
Computational Efficiency
Uses deep learning to approximate and replace computationally expensive simulation processes
Incomplete Data Handling
Infers missing velocity fields through masked inputs to handle incomplete data
Model Capabilities
Velocity Field Prediction
Physical Parameter Regression
Incomplete Data Inference
Use Cases
Fluid Mechanics Simulation
Velocity Field Evolution Prediction
Predicts changes in liquid velocity field over time based on initial conditions
Viscosity Coefficient Estimation
Estimates the viscosity coefficient of a liquid from observed data
Computational Fluid Dynamics
Simulation Replacement
Replaces traditional computational fluid dynamics simulations to reduce computational costs
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