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Nonlocal partial differential equations in dynamic density functional theory are hard to solve with standard methods. A new physics-informed neural network framework uses a modified Lorentzian activation to tackle these equations. It's unclear how well this will work outside theory.
“arXiv:2607.15291v1 Announce Type: cross Abstract: We develop a physics-informed neural network (PINN) framework for nonlocal partial differential equations arising in dynamic density functional theory (DDFT). Such equations are challenging …”
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Physics-Informed Neural Network, Dynamic Density Functional Theory, arXiv
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Aug 7, 2026