Data-driven modeling of constrained multibody dynamics remains challenged by (i) the training cost of Neural ODEs, which typically require backpropagation through an ODE solver, and (ii) error accumulation in rollout predictions. We introduce a Flow-Matching Neural ODE (FNODE) framework that learns the acceleration mapping directly from trajectory data by supervising accelerations rather than integrated states, turning training into a supervised regression problem and eliminating the ODE-adjoint/solver backpropagation bottleneck. Acceleration targets are obtained efficiently via numerical differentiation using a hybrid fast Fourier transform (FFT) and finite-difference (FD) scheme. Kinematic constraints are enforced through coordinate partitioning: FNODE learns accelerations only for the independent generalized coordinates, while the dependent coordinates are recovered by solving the position-level constraint equations. We evaluate FNODE on single and triple mass-spring-damper systems, a double pendulum, a slider-crank with and without friction, a vehicle model, and a cart-pole, and compare against MBD-NODE, LSTM, and fully connected baselines. We also compared the error contributions between numerical differentiation and Neural Network learned field mismatch in single-mass-spring-damper system, which proves the error of learned acceleration field is dominated by the error of Neural Network and confirms the robustness of numerical differentiation. Across these benchmarks, FN-ODE achieves improved prediction accuracy and training/runtime efficiency, while maintaining constraint satisfaction through the partitioning procedure. Our code and scripts are released as open source to support reproducibility and follow-on research.

We present a Total Lagrangian finite element framework for finite-deformation multibody dynamics. The framework combines a compact kinematic representation, a deformation-gradient-based formulation, an element-agnostic constitutive interface, and a systematic constraint-construction machinery for coupling deformable bodies through engineering joints. Within this setting, we derive the equations of motion for collections of deformable bodies and formulate their response in the presence of external loads, frictional contact forces, and constraint reaction forces. The framework accommodates field forces applied pointwise, over surfaces, or throughout volumes, and supports material models of practical interest, including Mooney-Rivlin, Neo-Hookean, and Kelvin-Voigt. A companion paper discusses the GPU-accelerated implementation of the framework outlined herein and reports on numerical experiments and benchmark results.

We report on an effort that led to POLAR3D, a set of digital assets that enhance the POLAR dataset of stereo images generated by NASA to mimic lunar lighting conditions. Our contributions are twofold. First, we have annotated each photo in the POLAR dataset, providing approximately 23 000 labels for rocks and their shadows. Second, we digitized several lunar terrain scenarios available in the POLAR dataset. Specifically, by utilizing both the lunar photos and the POLAR's LiDAR point clouds, we constructed detailed obj files for all identifiable assets. POLAR3D is the set of digital assets comprising of rock/shadow labels and obj files associated with the digital twins of lunar terrain scenarios. This new dataset can be used for training perception algorithms for lunar exploration and synthesizing photorealistic images beyond the original POLAR collection. Likewise, the obj assets can be integrated into simulation environments to facilitate realistic rover operations in a digital twin of a POLAR scenario. POLAR3D is publicly available to aid perception algorithm development, camera simulation efforts, and lunar simulation exercises.POLAR3D is publicly available at https://github.com/uwsbel/POLAR-digital.

High-fidelity embodied AI simulators provide realistic evaluation of complex robotic systems, but their computational cost limits their direct use for large-scale reinforcement learning campaigns. We advocate the use of less accurate but more expeditious simulations, which might draw on data-driven, e.g., neural dynamics, models. This contribution argues that the practical value of a neural dynamics model for complex robot control lies in learning the right abstraction: a reduced state that preserves the control-relevant physics of the high-fidelity system while enabling high-throughput policy learning. We develop a neural reduced dynamics (NRD) framework that separates the state the model propagates from what can be supplied as an input or recovered analytically, trains policies entirely inside the frozen learned model, and validates them back in the high-fidelity simulator. Two case studies instantiate it across three control tasks: terrain-aware HMMWV trajectory tracking on rigid, bumpy and deformable Continuum Representation Model (CRM) terrain; and goal reaching for a stock tracked vehicle and its front-mounted articulated arm. Every policy transfers back to the high-fidelity simulator. A single policy trained inside the terrain-conditioned dynamics model, and given no terrain input of its own, attains lower median and mean tracking error than both single-terrain specialists on all three terrains, including zero-shot bumpy terrain. Quantitatively, the tracked vehicle reaches 100 of 100 goals and the arm 97 of 100, with zero contacts or joint-limit violations. The NRD models advance roughly four orders of magnitude faster in simulated time than the high-fidelity simulator scenes they replace, making iterative on-policy learning practical and supporting neural reduced dynamics as a bridge between accurate but expensive physics simulation and scalable robot learning.

Optimizing the communication structure of large language model based multi-agent systems (LLM-MAS) has been shown to improve downstream performance and reduce token usage. Existing methods typically rely on randomly sampled training tasks. However, tasks may differ substantially in difficulty and domain, and thus they are not equally informative for updating communication structure, making optimization under limited training budgets often unstable and highly sensitive to the particular training set. To actively identify the most valuable tasks for communication-structure optimization, we propose an ensemble-based information-theoretic task selection framework. The proposed method estimates task informativeness by how much a candidate task changes the distribution over graph parameters, using ensemble Kalman inversion as an efficient and derivative-free approximation of the corresponding Bayesian update. The resulting estimator is especially suitable for black-box and noisy multi-agent systems. To enhance scalability, we construct a compact candidate pool through embedding-based representative selection and combine the informative selection with surrogate modeling and batch Thompson sampling. We validate our method in both benign settings and settings with agent attacks, demonstrating its effectiveness for communication-structure optimization under constrained computational budgets.

Particle heat exchangers are a promising solution for the concentrated solar power systems. However, their optimal design is hindered by the complex behavior of dense granular flow and the lack of computational design tools. This paper introduces a topology optimization framework specifically tailored for granular heat transfer systems, employing a steady-state one-momentum equation continuum model based on mu (I) rheology. The model captures main granular dynamics while remaining computationally efficient and compatible with adjoint-based optimization. Validation against discrete element method simulations and experiments supports the model's predictive accuracy. Integrating granular flow physics and topology optimization, this work establishes a scalable and practical design methodology for next-generation particle heat exchangers and advances topology optimization techniques for dense granular flow. At the design flow rate, the topology-optimized configuration achieves thermal conductance improvements of 226% and 141% over conventional parallel plate and cylindrical pin-fin designs respectively, as assessed by experiments.

Autonomous leveling of granular materials is a ubiquitous yet challenging operationin automated construction due to the complex physics governing the soil–tool interaction. This paper outlines a simulation-driven framework for optimizing low-level bulldozer blade control (pitch and height) to enhance leveling performance. The approach uses high-fidelity, physics-based simulations to generate training data. This data informs a neural network based reduced-order model that accurately predicts both the terrain evolution and the leveling operation duration in response to blade actions. A gradient-based, multiobjective optimization algorithm then utilizes the reduced-order model to determine optimal, time-varying blade control profiles, managing the trade-off between leveling flatness and operation time. The proposed method augments the state-of-the-art by producing policies that can readily level arbitrary soil pile configurations while avoiding vehicle immobilization and achieving better leveling efficiency. The system exhibits robustness to variations in initial pile geometry, and offers explicit control over the trade-off between leveling quality and operational efficiency. By integrating high-fidelity physics into the controller design and providing an open-source simulation pipeline, this work provides a low-level control solution that complements existing global path planning algorithms for autonomous construction operations. The project's resources, including code and media demonstration, are available at: https://uwsbel.github.io/Autonomous-Leveling/ .

Large-scale simulation studies can provide invaluable insights across computational engineering efforts, but they are often computationally demanding, requiring the use of distributed computing, which is itself not a simple task. Chrono::Ray addresses this challenge by integrating the high-fidelity multibody dynamics simulation engine Chrono with the open-source distributed computing platform Ray. The result is a modular workflow framework providing user-friendly abstractions for large-scale engineering simulation studies, supporting scalable orchestration of large ensembles of simulation trials without requiring users to directly manage distributed infrastructure. The current capabilities of the framework are demonstrated through two representative examples: parameter recovery for a multibody lunar lander model, and design of experiments for parameters of a continuum terramechanics model. Chrono::Ray is a part of the larger Project Chrono ecosystem and is released as an open-source software package, with source code available at https://github.com/uwsbel/chrono-ray.git.

This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants helping users with the effective use of a simulation tool? In this case study, the “simulation tool” considered is PyChrono, an open source multi-physics dynamics engine for multibody systems. We present a framework for refining and customizing both open- and closed-source LLMs to harness the power of AI in generating scripts that perform PyChrono virtual experiments. We refine and customize several classes of LLMs through a process that leads to a quantifiable improvement in the quality of the generated PyChrono simulation scripts. These scripts can range from simple single-pendulum simulations to complex virtual experiments involving full vehicles on deformable terrain. While the generated scripts are rarely perfect, they often serve as strong starting points for the user to modify and improve on. Additionally, the LLM can answer specific API questions about the simulator, or recommend modeling approaches. The framework discussed is general and can be applied to lower the entry barrier for simulation tools associated with other application domains.

The growing relevance of mechatronic applications, characterized by the tight interaction between mechanical and electrical domains, is driving the demand for accurate and efficient simulation tools. These simulations are particularly useful in robotics and automation, where mechanical sub-assemblies are coupled with various electric and electromagnetic devices, e.g. actuators or sensors. To support this class of applications, we present Chrono::Electronics - an open-source co-simulation solution for electro-mechanical systems. The library is designed to couple the multibody dynamics open-source engine Project Chrono, with the versatile open-source circuit simulator NGSpice, to solve complex electro-mechanical dynamic interactions. In this contribution we present the main features of the co-simulation library for electro-mechanical systems, featuring a fully reorganized structure and a renovated object-oriented architecture. The new framework introduces dedicated mechanisms for model and data handling, which improve the computational efficiency. In addition, the modular design of the utility enhances simulations results reliability and broadens the spectrum of possible applications through the introduction of subclasses for handling pre-defined, user-defined, and manufacturer-defined circuits. Beyond validating the proposed environment, we propose a novel methodology previously unexplored in this field, for the characterization of brushed DC motor parameters, based on a Bayesian Monte Carlo approach. Unlike existing techniques reported in the literature, this strategy provides a fundamentally different perspective on parameter estimation for real case applications. The method has been applied to an industrial DC motor, yielding highly accurate results and further demonstrating the framework's scalability and potential across electro-mechanical power applications where reliable modelling of electro-mechanical interactions is crucial.

While simulation is vital for optimizing robotic systems, the cost of modeling deformable terrain has long limited its use in full-vehicle studies of off-road autonomous mobility. For example, Discrete Element Method (DEM) simulations are often confined to single-wheel tests, which obscures coupled wheel-vehicle-controller interactions and prevents joint optimization of mechanical design and control. This paper presents a Bayesian optimization framework that co-designs rover wheel geometry and steering controller parameters using high-fidelity, full-vehicle closed-loop simulations on deformable terrain. Using the efficiency and scalability of a continuum-representation model (CRM) for terramechanics, we evaluate candidate designs on trajectories of varying complexity while towing a fixed load. The optimizer tunes wheel parameters (radius, width, and grouser features) and steering PID gains under a multi-objective formulation that balances traversal speed, tracking error, and energy consumption. We compare two strategies: simultaneous co-optimization of wheel and controller parameters versus a sequential approach that decouples mechanical and control design. We analyze trade-offs in performance and computational cost. Across 3,000 full-vehicle simulations, campaigns finish in five to nine days, versus months with the group's earlier DEM-based workflow. Finally, a preliminary hardware study suggests the simulation-optimized wheel designs preserve relative performance trends on the physical rover. Together, these results show that scalable, high-fidelity simulation can enable practical co-optimization of wheel design and control for off-road vehicles on deformable terrain without relying on prohibitively expensive DEM studies. The simulation infrastructure (scripts and models) is released as open source in a public repository to support reproducibility and further research.

We present the numerical methods and GPU-accelerated implementation underlying a Total Lagrangian finite element framework for finite-deformation flexible multibody dynamics, introduced in the companion paper [1]. The framework supports 10-node quadratic tetrahedral (T10) elements and ANCF beam and shell elements, with quadrature-based hyperelastic response (St. Venant-Kirchhoff and Mooney-Rivlin) and an optional Kelvin-Voigt viscous stress contribution. Time stepping employs a velocity-based implicit backward-Euler scheme, yielding a nonlinear residual in velocity that couples inertia, internal and external forces, and bilateral constraints. Constraints are enforced via an augmented Lagrangian method (ALM), structured as an outer loop alternating an inner velocity solve with a dual-ascent multiplier update. We introduce a two-stage GPU parallelization strategy for internal force and tangent stiffness evaluation, and provide two inner solvers: a first-order AdamW optimizer and a second-order Newton solver that assembles and factorizes a sparse global Hessian on the GPU using cuDSS. A fixed-sparsity matrix strategy eliminates repeated symbolic analysis and enables efficient numerical refactorization across Newton iterations. For collision detection, we present a GPU-native two-thread asynchronous algorithm operating on triangle soups, avoiding bounding-volume hierarchies entirely. Systematic scaling benchmarks across all three supported element types and six mesh resolutions show that the Newton solver achieves approximately one order of magnitude reduction in real-time factor relative to CPU baselines at the largest resolutions tested. The frictional contact model is validated against closed-form rigid-body predictions through quasi-static and dynamic impact unit tests.

High-fidelity physics simulation is essential for closing the sim-to-real gap in robotics and complex mechanical systems. However, the computational overhead of high-fidelity engines often limits their use in data-intensive tasks like Reinforcement Learning (RL) and global optimization. We introduce Chrono-Gymnasium, a distributed computing framework that scales the high-fidelity multi-body dynamics of Project Chrono across large-scale computing clusters. Built upon the Ray framework, Chrono-Gymnasium provides a standardized Gymnasium interface, enabling seamless integration with modern machine learning libraries while providing built-in synchronization and messaging primitives for distributed execution. We demonstrate the framework's capabilities through two distinct case studies: (1) the training of an RL agent for autonomous robotic navigation in complex terrains, and (2) the Bayesian Optimization of a planetary lander's design parameters to ensure landing stability. Our results show that Chrono-Gymnasium reduces wall-clock time for high-fidelity simulations without sacrificing physical accuracy, offering a scalable path for the design and control of complex robotic systems.

We introduce SimBench, a benchmark designed to evaluate the proficiency of simulator-oriented LLMs (S-LLMs) in generating digital twins (DTs) that can be used in simulators for virtual testing. Given a collection of S-LLMs, this benchmark ranks them according to their ability to produce high-quality DTs. We demonstrate this by comparing over 33 open- and closed-source S-LLMs. Using multi-turn interactions, SimBench employs an LLM-as-a-judge (J-LLM) that leverages both predefined rules and human-in-the-loop guidance to assign scores for the DTs generated by the S-LLM, thus providing a consistent and expert-inspired evaluation protocol. The J-LLM is specific to a simulator, and herein the proposed benchmarking approach is demonstrated in conjunction with the open-sourceChrono multi-physics simulator. Chrono provided the backdrop used to assess an S-LLM in relation to the latter’s ability to create digital twins for multibody dynamics, finite element analysis, vehicle dynamics, robotic dynamics, and sensor simulations. The proposed benchmarking principle is broadly applicable and enables the assessment of an S-LLM’s ability to generate digital twins for other simulation packages, e.g., ANSYS, ABAQUS, OpenFOAM, StarCCM+, IsaacSim, and pyBullet.

This paper discusses Chrono's Continuous Representation Model (called herein Chrono::CRM), a general-purpose, scalable, and efficient simulation solution for terramechanics problems. Built on Chrono's Smoothed Particle Hydrodynamics (SPH) framework, Chrono::CRM moves beyond semi-empirical terramechanics approaches, e.g., Bekker-Wong/Janosi-Hanamoto, to provide a physics-based model able to address complex tasks such as digging, grading, as well as interaction with deformable wheels and complex grouser/lug patterns. The terramechanics model is versatile in that it allows the terrain to interact with both rigid and flexible implements simulated via the Chrono dynamics engine. We validate Chrono::CRM against experimental data from three physical tests, including one involving NASA's MGRU3 rover. In addition, the simulator is benchmarked against a high-fidelity Discrete Element Method (DEM) simulation of a digging scenario involving the Regolith Advanced Surface Systems Operations Robot (RASSOR). Being GPU-accelerated, Chrono::CRM achieves computational efficiency comparable to that of semi-empirical simulation approaches for terramechanics problems. Through an “active domains” implementation, Chrono::CRM can handle terrain stretches up to 10 km long with 100 million SPH particles at near interactive rates, making high-fidelity off-road simulations at large scales feasible. As a component of the Chrono package, the CRM model is open source and released under a BSD-3 license. All models and simulations used in this contribution are available in a public GitHub repository for reproducibility studies and further research.

Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit physical constraints: contacts drift, shapes distort, and motion loses consistency. We present ChronoAgentic, a multi-agent framework that instead constructs the world as executable simulation code. The plan agent converts the natural-language prompt into a structured scene plan that the user can inspect and approve. The code agent implements the plan as an executable PyChrono program, grounded in a curated skill library, a generative 3D asset pipeline, and retrieval over the simulator source. After execution, the visual-analysis agent describes the rendered rollout, while deterministic physics checks scan the simulated trajectories for anomalies. The review agent evaluates this execution evidence, and the code agent iteratively repairs the program until it satisfies the plan objectives and physical constraints. On a suite of 80 demos selected from the PhyWorldBench benchmark, ChronoAgentic satisfies the benchmark's full correctness criterion–semantic adherence and physical correctness judged jointly—on 82.5

Recently, the integration of advanced simulation technologies with artificial intelligence (AI) is revolutionizing science and engineering research. ChronoLlama introduces a novel framework that customizes the open-source LLMs, specifically for code generation, paired with PyChrono for multi-physics simulations. This integration aims to automate and improve the creation of simulation scripts, thus enhancing model accuracy and efficiency. This combination harnesses the speed of AI-driven code generation with the reliability of physics-based simulations, providing a powerful tool for researchers and engineers. Empirical results indicate substantial enhancements in simulation setup speed, accuracy of the generated codes, and overall computational efficiency. ChronoLlama not only expedites the development and testing of multibody systems but also spearheads a scalable, AI-enhanced approach to managing intricate mechanical simulations. This pioneering integration of cutting-edge AI with traditional simulation platforms represents a significant leap forward in automating and optimizing design processes in engineering applications.

The wheel-soil interaction has great impact on the dynamics of off-road vehicles in terramechanics applications. The soil contact model (SCM), which anchors an empirical method to characterize the frictional contact between a wheel and soil, has been widely used in off-road vehicle dynamics simulations because it quickly produces adequate results for many terramechanics applications. The SCM approach calls for a set of model parameters that are obtained via a bevameter test. This test is expensive and time consuming to carry out, and in some cases difficult to set up, e.g., in extraterrestrial applications. We propose an approach to address these concerns by conducting the bevameter test in simulation, using a model that captures the physics of the actual experiment with high fidelity. To that end, we model the bevameter test rig as a multibody system, while the dynamics of the soil is captured using a discrete element model (DEM). The multibody dynamics-soil dynamics co-simulation is used to replicate the bevameter test, producing high fidelity ground truth test data that is subsequently used to calibrate the SCM parameters within a Bayesian inference framework. To test the accuracy of the resulting SCM terramechanics, we run single wheel and full rover simulations using both DEM and SCM terrains. The SCM results match well with those produced by the DEM solution, and the simulation time for SCM is two to three orders of magnitude lower than that of DEM. All simulations in this work are performed using Chrono, an open-source, publicly available simulator. The scripts and models used are available in a public repository for reproducibility studies and further research.

This contribution reports on a software framework that uses physically-based rendering to simulate camera operation in lunar conditions. The focus is on generating synthetic images qualitatively similar to those produced by an actual camera operating on a vehicle traversing and/or actively interacting with lunar terrain, e.g., for construction operations. The highlights of this simulator are its ability to capture (i) light transport in lunar conditions and (ii) artifacts related to the vehicle-terrain interaction, which might include dust formation and transport. The simulation infrastructure is built within an in-house developed physics engine called Chrono, which simulates the dynamics of the deformable terrain-vehicle interaction, as well as fallout of this interaction. The Chrono::Sensor camera model draws on ray tracing and Hapke Photometric Functions. We analyze the performance of the simulator using two virtual experiments featuring digital twins of NASA's VIPER rover navigating a lunar environment, and of the NASA's RASSOR excavator engaged into a digging operation. The sensor simulation solution presented can be used for the design and testing of perception algorithms, or as a component of in-silico experiments that pertain to large lunar operations, e.g., traversability, construction tasks.