neural ODEs

2 items

RESEARCHarXiv CS.LG·4/8/2026

Enhancing sample efficiency in reinforcement-learning-based flow control: replacing the critic with an adaptive reduced-order model

Este trabalho introduz uma estrutura de aprendizado por reforço baseada em modelo de ordem reduzida (ROM) adaptativo para controle de fluxo ativo. Ele visa melhorar a eficiência de amostragem do DRL, substituindo o crítico por um ROM que estima gradientes e se atualiza continuamente com novos dados.

Sample Efficiency reinforcement learning Flow Control Reduced-Order Models

RESEARCHarXiv CS.LG·4/30/2026

Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time

This research addresses challenges in continuous-time causal inference due to hidden confounders, demonstrating that observability of latent dynamics is crucial for identifying dynamic treatment effects. It proposes Observable Neural ODEs (ObsNODEs), a novel model for causal forecasting by learning reconstructible continuous-time dynamics.

forecasting continuous-time models causal inference neural ODEs