Physics-constrained deep learning approach for solving forward and inverse thermo-mechanical coupling problems

Key Points

• Models thermo-mechanical coupling efficiently using deep learning, improving predictive accuracy.
• Key metrics include reduction in error rates by 25% using this physics-based approach.
• Approach involves a physics-constrained deep learning model to solve complex forward and inverse problems.
• This method may enable better solutions in engineering applications, while further validation is necessary.