… arbitrary Lagrangians with neural networks, … Lagrangians using neural networks. We
demonstrate that they can model complex physical systems which Hamiltonian Neural Networks (…

… accurately modelled by neural networks with 100 times less … Lagrangians in Cartesian
coordinates via explicit constraints using networks that we term Constrained Hamiltonian Neural …

… to neural network bounds (Dvijotham et al., 2018) are based on Lagrangian relaxations, we
derive a new family of optimization problems for neural network bounds through Lagrangian …

… In the generalized Lagrange’s equations, the Lagrangian L holds the same physical … neural
network suitable for non-conservative systems using the framework of generalized Lagrange’…

… , a neural network can be trained solely based on initial and boundary conditions. Neural
networks are … We build on the aforementioned work in order to develop an Updated Lagrangian …

… are introduced to parametrize the neuron activations. By framing the optimization of
neural networks in the Lagrangian framework, where neural computations are expressed as …

… and system Lagrangian from high-… neural network and initial states are integrated
forward using the equations of motion determined by the system Lagrangian and Euler-Lagrange …

… To obtain a favorable coupling between input and states, we suggest first deriving the
equations of motion with the Lagrange formalism and then integrating them into a neural network …

… To address this challenge, here, we propose a Lagrangian graph neural network (LGnn), …
, which is then aggregated to compute the total Lagrangian of the system. To model realistic …

… neural networks based on an augmented Lagrangian framework that only requires finding
approximate stationary points of the Lagrangian … structure of feedforward neural networks, we …