By Bhalchandra D. Thatte, Universidade Federal de Minas Gerais (UFMG) Brasil

abstract:
Real world systems in sustainable energy, engineering, and other scientific domains are often governed by complex,non-linear, physics-based mathematical models. However, these traditional models frequently face challenges related to accuracy and interpretability. Modeling such complex physical systems such as convective heat and fluid flow phenomena in porous media requires an integrity of physics and data to achieve more reliable and accurate model development. In this research talk, I will present developments of data and physics guided deep learning in industrial and other scientific domains. These hybrid techniques offer accurate and efficient methods for developing surrogate models that significantly reduce computational cost while preserving the interpretability of multiphysics influence on the complex system, particularly in areas such as solar power collectors and subsurface flow operations (oil
production). This presentation will highlight ongoing efforts to enhance model generalization under a limited data scenario and to design scientifically grounded, computationally tractable models. Collectively, these works demonstrates a pathway toward building reliable, scalable, and interpretable data-driven solutions for computational modeling and engineering applications.