5 citations · 5 across the 2 of their papers we have counts for
3 papers
Growing Neural Networks: Dynamic Evolution through Gradient Descent
Anil Radhakrishnan, John F. Lindner, Scott T. Miller +2
In contrast to conventional artificial neural networks, which are structurally static, we present two approaches for evolving small networks into larger ones during training. The f…
Forecasting Hamiltonian dynamics without canonical coordinates
Anshul Choudhary, John F. Lindner, Elliott G. Holliday +3
Conventional neural networks are universal function approximators, but because they are unaware of underlying symmetries or physical laws, they may need impractically many training…
Mastering high-dimensional dynamics with Hamiltonian neural networks
Scott T. Miller, John F. Lindner, Anshul Choudhary +2
We detail how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimension…