The Rheological Universal Differential Equation (RUDE) framework embeds neural networks within a frame-indifferent tensorial constitutive backbone and so enables data-driven discovery of complex material rheological behavior. However, the flexibility that makes the RUDE framework attractive also makes it difficult to d...
Mohua Das, Nicholas King, Navid Azizan et al.· 0 citations
Diffusion and flow models provide expressive policy classes for online reinforcement learning (RL), enabling multimodal behaviors and improved performance. However, training these policies remains challenging: the critic specifies the desired policy as an unnormalized Boltzmann density but does not provide direct sampl...
Ze-Yang Li, Yu-Nan Wang, Risheek Garrepalli et al.· 0 citations
This paper proposes a safe meta-RL framework that explicitly accounts for safety during adaptation, and develops a safe meta-RL algorithm that learns the safety value function and leverages it for safety filtering and constrained policy optimization.
The paradigm from isolated losses to iterative optimization over canonical models: population minimizers of standard conditional matching for terminal densities and approximate updates and define critical-point consistency as vanishing tangential displacement if and only if $\rho=\pi$.
Ze-Yang Li, Yu-Nan Wang, Paolo Giaretta et al.· 0 citations
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