LFNO is a dual-branch neural operator combining Laplace and Fourier methods to explicitly decompose and model transient and steady-state dynamics, outperforming baselines on ODE benchmarks and remaining competitive on PDEs.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper proposes a two-dimensional phase diagram of equation discoverability and the representation-evaluation-optimization (REO) framework to organize data-driven differential equation discovery across physical systems.
citing papers explorer
-
LFNO: Bridging Laplace and Fourier via Transient-Steady Decomposition
LFNO is a dual-branch neural operator combining Laplace and Fourier methods to explicitly decompose and model transient and steady-state dynamics, outperforming baselines on ODE benchmarks and remaining competitive on PDEs.
-
Data-driven discovery of governing differential equations across physical systems
The paper proposes a two-dimensional phase diagram of equation discoverability and the representation-evaluation-optimization (REO) framework to organize data-driven differential equation discovery across physical systems.