Physics-informed neural networks and beyond: enforcing physical constraints in quantum dissipative dynamics†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-09-05 DOI:10.1039/D4DD00153B
Arif Ullah, Yu Huang, Ming Yang and Pavlo O. Dral
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Abstract

Neural networks (NNs) accelerate simulations of quantum dissipative dynamics. Ensuring that these simulations adhere to fundamental physical laws is crucial, but has been largely ignored in the state-of-the-art NN approaches. We show that this may lead to implausible results measured by violation of the trace conservation. To recover the correct physical behavior, we develop physics-informed NNs (PINNs) that mitigate the violations to a good extent. Beyond that, we propose a novel uncertainty-aware approach that enforces perfect trace conservation by design, surpassing PINNs.

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物理信息神经网络及其他:在量子耗散动力学中执行物理约束
神经网络(NN)可加速量子耗散动力学模拟。确保这些模拟符合基本物理定律至关重要,但最先进的神经网络方法在很大程度上忽视了这一点。我们的研究表明,这可能会导致违反痕量守恒的难以置信的结果。为了恢复正确的物理行为,我们开发了物理信息 NN(PINN),可以很好地减轻违反物理规律的情况。除此以外,我们还提出了一种新颖的不确定性感知方法,通过设计实现完美的轨迹守恒,超越了 PINNs。
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