Dynamic Modeling
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Related Articles from SNS
Hybrid Dynamics Modeling for a Flexible 2-DoF Robotic Arm
arXiv:2606.02969v1 Announce Type: new Abstract: This paper examines three approaches for modeling the dynamics of a flexible-link 2-DoF robotic arm to address unmodeled dynamics not captured by rigid-body models. Two physics informed models combine rigid-body dynamics (RBD) formulations with a Gaussian Mixture Model (GMM) to capture residual model errors and linkage flexibility. A kinematics-based regression model serves as a purely data-driven baseline.
CAPE: Control Algorithm Performance Evaluation under Learned Vehicle Dynamics Models
arXiv:2606.05480v1 Announce Type: new Abstract: We propose the Control Algorithm Performance Evaluation (CAPE) framework, a systematic methodology for benchmarking racing controllers under our proposed learned enhanced physics model (EPM). The proposed framework enables cross-controller comparison by evaluating five closed-loop control architectures. We further compare our proposed EPM with two state-of-the-art learned vehicle dynamics models:
Dynamic Modeling of Magneto-Active Grounding Electrodes under Transient Conditions
arXiv:2606.06506v1 Announce Type: new Abstract: Grounding systems operating under transient electromagnetic conditions exhibit highly nonlinear behavior influenced by electromagnetic propagation, soil conductivity variations, thermal diffusion, moisture transport, and ionization phenomena. Conventional grounding analyses generally rely on static resistance formulations that neglect the coupled dynamics governing advanced grounding technologies. In particular, rigorous dynamic models...
Circuit-Inspired High-Order Neural Networks with Unified Neural Dynamics Modeling for PDE Solving and Visual Perception
Announce Type: replace Abstract: Deep networks often rely on architectural heuristics to shape representation evolution, limiting their ability to model data governed by intrinsic dynamics. We present the Circuit-inspired High-Order Neural Network (CHONN), a modular framework that treats representation evolution as a latent potential process and increases its effective order through Kirchhoff-inspired cascade composition. A single Kirchhoff Neural Cell implements a stable first-order update,...
World2Act: Latent Action Post-Training from World Model Dynamics
arXiv:2603.10422v2 Announce Type: replace Abstract: World Models (WMs) offer a promising mechanism for post-training Vision-Language-Action (VLA) policies by providing dynamics priors that improve generalization under task and scene variation. However, most WM-based post-training methods rely on pixel-space supervision, making policies sensitive to visual artifacts introduced by imperfect WM rollouts. We present World2Act, a latent-space post-training framework that transfers WM dynamics to...
Identifying sensitivity-dominant parameters via active subspaces in reduced-order modeling of fluid dynamics
new Abstract: Reduced-order models (ROMs) are widely employed to describe complex system dynamics when simulations with full-order models (FOMs) are computationally prohibitive. This study presents POD-AS-PRS, a novel model-reduction framework based on the active subspaces (AS) technique, which performs dimensionality reduction in both the state and parameter spaces, enabling efficient and high-fidelity approximations of quantities of interest (QoI). The approach employs proper orthogonal...
Modelling Opinion Dynamics at Scale with Deep MARL
arXiv:2606.07487v1 Announce Type: new Abstract: Modelling opinion dynamics typically relies on hand-crafted local interaction rules to study emergent macroscopic phenomena such as consensus and polarisation. In contrast, multi-agent reinforcement learning (MARL) enables agents to learn such behaviours directly by optimising simple rewards. To explore the potential of MARL for opinion dynamics, we introduce a GPU-accelerated consensus and truth-finding game that scales to populations of up to...
Evaluating Large Language Models in Dynamic Clinical Decision-Making with Standardized Patient Cases
Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed as clinical agents, yet static, single-turn benchmarks cannot capture how a model dynamically delivers care across an encounter: gathering information, planning treatment, and adapting longitudinal management across successive patient states. Medical education has long addressed an analogous challenge through standardized patients (SPs): trained actors who consistently portray clinical cases, enabling...
What Leads to Administrative Bloat? A Dynamic Model of Administrative Cost and Waste
Announce Type: replace Abstract: The functioning of complex systems depends on the coordination of diverse components, often supported by regulatory structures that incur costs. In human organizations, such costs manifest as administrative burden, which has been rising despite often reducing efficiency. Classic explanations point to bureaucrat self-interest or regulation, yet they do not explain variation across organizations or clarify how this burden can be reduced.
DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity
arXiv:2606.09043v1 Announce Type: new Abstract: Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a dynamic reweighting framework for mitigating shortcut learning in reward model training. Unlike static shortcut heuristics, DynaCF measures shortcut sensitivity online during optimization by applying semantics-preserving counterfactual perturbations and tracking the resulting margin shifts and...