Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning structure preserving brackets for forecasting
  irreversible processes   Forecasting of time-series data requires imposition of inductive biases to
obtain predictive extrapolation, and recent works have imposed
Hamiltonian/Lagrangian form to preserve structure for systems with reversible
dynamics. In this work we present a novel parameterization of dissipative
brackets from metriplectic dynamical systems appropriate for learning
irreversible dynamics with unknown a priori model form. The process learns
generalized Casimirs for energy and entropy guaranteed to be conserved and
nondecreasing, respectively. Furthermore, for the case of added thermal noise,
we guarantee exact preservation of a fluctuation-dissipation theorem, ensuring
thermodynamic consistency. We provide benchmarks for dissipative systems
demonstrating learned dynamics are more robust and generalize better than
either "black-box" or penalty-based approaches.

10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th