7 papers
Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
Paulo Akira F. Enabe, Rodrigo Provasi
A unified framework for learning under covariate shift is presented, in which a constrained density-ratio network approximates the Radon-Nikodym derivative and fe…
Profile-Then-Reason: Bounded Semantic Complexity for Tool-Augmented Language Agents
Paulo Akira F. Enabe
Large language model agents that use external tools are often implemented through reactive execution, in which reasoning is repeatedly recomputed after each observation, increasing…
An Investigation of Stabilization Scaling in Finite-Strain Virtual Element Methods for Hyperelasticity
Paulo Akira F. Enabe, Rodrigo Provasi
Low-order virtual element methods (VEM) compute a consistent finite-strain contribution through polynomial projections and rely on stabilization to control the unresolved modes in…
Mass-Lumped Virtual Element Method with Strong Stability-Preserving Runge-Kutta Time Stepping for Two-Dimensional Parabolic Problems
Paulo Akira F. Enabe, Rodrigo Provasi
This paper presents a mass-lumped Virtual Element Method (VEM) with explicit Strong Stability-Preserving Runge--Kutta (SSP-RK) time integration for two-dimensional parabolic proble…
Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems
Paulo Akira F. Enabe, Rodrigo Provasi
This work presents a Virtual Element Method (VEM) formulation tailored for two-dimensional axisymmetric problems in linear elasticity. By exploiting the rotational symmetry of the…
A Hybrid Virtual Element Method and Deep Learning Approach for Solving One-Dimensional Euler-Bernoulli Beams
Paulo Akira F. Enabe, Rodrigo Provasi
A hybrid framework integrating the Virtual Element Method (VEM) with deep learning is presented as an initial step toward developing efficient and flexible numerical models for one…