Phyiscally consistent simulation of quantum dissipative dynamics with neural networks 

This video presents the recent research progress of Assistant Professor Arif Ullah from Anhui University, Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics. He will highlight his recent work, published in Digital Discovery which addresses the issue of trace conservation in machine learning-based quantum dissipative dynamics. In this work, they demonstrate that existing machine learning approaches often violate trace conservation, leading to inaccurate results. To address this, they propose a Physics-informed neural network and a novel uncertainty-aware hard coded constraint approach that enforces perfect trace conservation by design.
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