arXiv subjects

Ningyu Yan

Publications and source records attributed to Ningyu Yan.

4 recordsLinked to original sources

arXiv2026

OmniRemesh: Adaptive and Quasi-differentiable Remeshing for Crystal Plasticity Simulation and Inverse Parameter Calibration under Large Deformation

Large-deformation crystal plasticity finite element method (CPFEM) simulations are often limited by accumulated mesh distortion, which degrades accuracy and numerical stability, while adaptive remeshing introduces discrete topology changes that impede gradient-based inverse analysis. We present OmniRemesh, a unified framework that addresses these forward and inverse challenges through two developments. First, a structure-driven remeshing method dynamically redistributes local mesh resolution according to both microstructural geometry and the evolving mechanical state. By refining grain boundaries and localized deformation regions while retaining a coarser mesh elsewhere, the method maintains mesh quality and physical consistency, improves the accuracy and robustness of large-deformation calculations, and resolves grain-scale heterogeneity without uniformly dense discretization. Second, a frozen-remeshing-branch strategy locally fixes the mesh sequence within a parameter trust region and periodically updates it as the parameters evolve. This treatment provides approximate automatic-differentiation sensitivities despite topology changes, enabling efficient inverse calibration of constitutive parameters against both macroscopic and local observables. Numerical examples demonstrate accurate and stable CPFEM simulations up to 80\% tensile deformation. The inverse calibration successfully recovers both macroscopic and local responses. OmniRemesh thus provides a practical framework for large-deformation CPFEM and remeshing-aware constitutive calibration.

cs.CE↗

arXiv2026

GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models

Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on perceptual similarity or human judgments, providing limited insight into which physical principles or parameters are violated. We introduce GAUGE, a real-world-grounded diagnostic benchmark for jointly evaluating how numerical simulators and generative video world models reproduce or deviate from real-world physics. It comprises 22 controlled task families covering rigid bodies, flexible cables, textiles, and volumetric deformable objects. Grounded in real-world trajectories and paired with calibrated physical metadata, uncertainty annotations, and task-specific observables, these tasks cover fundamental physical processes including collision, friction, momentum transfer, oscillation, self-contact, and deformation across diverse materials and conditions. We benchmark Isaac Sim, Genesis, and Newton on 14 task families using generalized trajectory errors, and evaluate 6 image-to-video models on 5 rigid-body tasks by testing physical-law consistency and the temporal stability of inferred parameters. Our results reveal no uniformly faithful physics engine, with the largest discrepancies arising in impulsive contact, rapid textile motion, and volumetric deformation. We further find that video world models can produce trajectories with the expected equation form while recovering incorrect accelerations, momentum transfer, and oscillation timing. GAUGE lays the groundwork for developing more physically faithful simulators and world models for embodied intelligence.

cs.AI↗

arXiv2026

AlloyVAE: A generative model for complex probabilistic field-to-field relationships in alloys

The inherent compositional heterogeneity of multi-principal element alloys (MPEAs) gives rise to complex, spatially varying mechanical fields that cannot be uniquely determined from coarse-grained composition descriptors. This non-uniqueness introduces intrinsically probabilistic structure-property relationships, posing a fundamental challenge to conventional deterministic modeling and machine learning approaches that collapse such mappings into average predictions. Here, we present AlloyVAE, a physics-informed generative framework that learns the full conditional distribution of mechanical fields from microstructural inputs. Built upon a conditional variational autoencoder architecture, the model incorporates learned smoothing operators to enhance functional regularity and a self-consistency mechanism to enforce physical plausibility. Trained on atomistic simulation data, AlloyVAE accurately predicts distributions of residual stress fields from composition and short-range order, and enables the generation of multiple physically consistent realizations under identical input conditions. Beyond forward prediction, the framework supports inverse design by optimizing composition fields to achieve targeted mechanical responses, and is extensible to coupled mappings involving eigenstrain. By capturing one-to-many structure-property relationships in heterogeneous materials, this work establishes a probabilistic paradigm for materials modeling and design, providing a scalable alternative to conventional simulations for navigating high-dimensional compositional spaces.

cond-mat.mtrl-sci↗

arXiv2026

Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment

Visuotactile sensors are indispensable for contact-rich robotic manipulation tasks. However, policy learning with tactile feedback in simulation, especially for online reinforcement learning (RL), remains a critical challenge, as it demands a delicate balance between physics fidelity and computational efficiency. To address this challenge, we present Tac2Real, a lightweight visuotactile simulation framework designed to enable efficient online RL training. Tac2Real integrates the Preconditioned Nonlinear Conjugate Gradient Incremental Potential Contact (PNCG-IPC) method with a multi-node, multi-GPU high-throughput parallel simulation architecture, which can generate marker displacement fields at interactive rates. Meanwhile, we propose a systematic approach, TacAlign, to narrow both structured and stochastic sources of domain gap, ensuring a reliable zero-shot sim-to-real transfer. We further evaluate Tac2Real on the contact-rich peg insertion task. The zero-shot transfer results achieve a high success rate in the real-world scenario, verifying the effectiveness and robustness of our framework. The project page is: https://ningyurichard.github.io/tac2real-project-page/

cs.RO↗