REVIEW 6 cited by
Hamiltonian Neural Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. We evaluate our models on problems where conservation of energy is important, including the two-body problem and pixel observations of a pendulum. Our model trains faster and generalizes better than a regular neural network. An interesting side effect is that our model is perfectly reversible in time.
Forward citations
Cited by 6 Pith papers
-
Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks
A random-feature Hamiltonian neural network trained only on regular dynamics extrapolates the onset and growth of chaos in four Hamiltonian systems.
-
Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity
Q-pHNNs learn classical conservative and dissipative dynamics by mapping the port-Hamiltonian J matrix to unitary gates and the R matrix to mid-circuit measurement nonlinearity, enforcing structure by construction.
-
Learning the Brain's Dynamics as a Port-Hamiltonian System: A GNN-Surrogate Metriplectic Twin for Non-Equilibrium Cortical Dynamics and Closed-Loop Neuromodulation
A port-Hamiltonian GNN trained on EEG phasors matches the cortex's avalanche-branching ratio (σ≈1) but misses its 1/f spectrum and long-range correlations.
-
Gradient Networks for Universal Magnetic Modeling of Synchronous Machines
A gradient-network model trained on sparse flux-linkage/current data reproduces the saturable, angle-periodic magnetic maps of a 5.6-kW synchronous machine while enforcing reciprocity, convexity, and smoothness by con...
-
Symmetry-preserving neural networks in lattice field theories
Translation- and gauge-equivariant neural networks (L-CNNs) predict Wilson loops, topological charge, and flux observables with orders-of-magnitude lower error than symmetry-breaking baselines, and neural gradient flo...
-
Foundation Models for Astrophysics
Astronomical 'foundation models' largely reuse transformers and self-supervised pretraining, but evidence of transfer to new instruments, populations, or tasks remains rare; the paper argues such evidence, not archite...
Discussion (0). Sign in to comment.