Forecasting high-dimensional dynamical systems is a fundamental challenge in various
fields, such as geosciences and engineering. Neural Ordinary Differential Equations …

Unrolling training trajectories over time strongly influences the inference accuracy of neural
network-augmented physics simulators. We analyze these effects by studying three variants …

Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant
features and improve the accuracy of classification at the same time. Feature selection is a …

An adjoint-based variational optimal mixed model (VOMM) is proposed for subgrid-scale
(SGS) closure in large-eddy simulation (LES) of turbulence. The stabilized adjoint LES …

Original scientific paper Abstract: In this paper, we present the Rat Swarm Optimization with
Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting …

Gradient-based optimization algorithms, where gradient information is extracted using
adjoint equations, are efficient but can quickly slow down when applied to unsteady and …

An adjoint-based framework is presented that measures exact sensitivity from high-fidelity
simulations of turbulent reacting flows. The framework leverages and extends state-of-the-art …

Unrolling training trajectories over time strongly influences the inference accuracy of neural
network-augmented physics simulators. We analyze these effects by studying three variants …

This paper presents a method for estimating output errors and adapting computational
meshes in simulations of unsteady turbulent flows. The chaotic nature of such problems …

Predicting the long-term behavior of chaotic systems remains a formidable challenge due to
their extreme sensitivity to initial conditions and the inherent limitations of traditional data …