# A Review of Differentiable Simulators

arXiv: https://arxiv.org/abs/2407.05560 · HTML: https://mlsanity.com/paper/2407.05560/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2024.

## Built on by (most cited first, 28 of 28)

- [LLM-as-a-Verifier: A General-Purpose Verification Framework](https://mlsanity.com/paper/2607.05391/) (2026)
- [Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation](https://mlsanity.com/paper/2412.12089/) (2024)
- [Quadrotor Navigation using Reinforcement Learning with Privileged Information](https://mlsanity.com/paper/2509.08177/) (2025)
- [Differentiable Simulation of Hard Contacts with Soft Gradients for Learning and Control](https://mlsanity.com/paper/2506.14186/) (2025)
- [Simulation-Ready Cluttered Scene Estimation via Physics-aware Joint Shape and Pose Optimization](https://mlsanity.com/paper/2602.20150/) (2026)
- [Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions](https://mlsanity.com/paper/2503.11269/) (2025)
- [Implicit Neural Differential Model for Spatiotemporal Dynamics](https://mlsanity.com/paper/2504.02260/) (2025)
- [Learning Particle Dynamics Subject to Rigid Body Manipulations Using Graph Neural Networks](https://mlsanity.com/paper/2509.03446/) (2025)
- [Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality](https://mlsanity.com/paper/2510.17709/) (2025)
- [Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX](https://mlsanity.com/paper/2606.01478/) (2026)
- [Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer](https://mlsanity.com/paper/2409.15710/) (2024)
- [Learning second-order TVD flux limiters using differentiable solvers](https://mlsanity.com/paper/2503.09625/) (2025)
- [An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer](https://mlsanity.com/paper/2503.10118/) (2025)
- [Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts](https://mlsanity.com/paper/2507.07348/) (2025)
- [Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters](https://mlsanity.com/paper/2508.13303/) (2025)
- [PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles](https://mlsanity.com/paper/2511.15522/) (2025)
- [Physics-Informed Spiking Neural Networks via Conservative Flux Quantization](https://mlsanity.com/paper/2511.21784/) (2025)
- [Active Convolved Illumination with Deep Transfer Learning for Complex Beam Transmission through Atmospheric Turbulence](https://mlsanity.com/paper/2512.19540/) (2025)
- [Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements](https://mlsanity.com/paper/2601.20496/) (2026)
- [RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning](https://mlsanity.com/paper/2603.22039/) (2026)
- [Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation](https://mlsanity.com/paper/2603.25068/) (2026)
- [Differentiable Power-Flow Optimization](https://mlsanity.com/paper/2603.28203/) (2026)
- [Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics](https://mlsanity.com/paper/2604.21456/) (2026)
- [Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark](https://mlsanity.com/paper/2605.01040/) (2026)
- [Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming](https://mlsanity.com/paper/2606.06094/) (2026)
- [Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics](https://mlsanity.com/paper/2609.08800/) (2026)
- [ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics](https://mlsanity.com/paper/2610.17419/) (2026)
- [TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch](https://mlsanity.com/paper/2610.18924/) (2026)

## Abstract (arXiv)

Differentiable simulators continue to push the state of the art across a range of domains including computational physics, robotics, and machine learning. Their main value is the ability to compute gradients of physical processes, which allows differentiable simulators to be readily integrated into commonly employed gradient-based optimization schemes. To achieve this, a number of design decisions need to be considered representing trade-offs in versatility, computational speed, and accuracy of the gradients obtained. This paper presents an in-depth review of the evolving landscape of differentiable physics simulators. We introduce the foundations and core components of differentiable simulators alongside common design choices. This is followed by a practical guide and overview of open-source differentiable simulators that have been used across past research. Finally, we review and contextualize prominent applications of differentiable simulation. By offering a comprehensive review of the current state-of-the-art in differentiable simulation, this work aims to serve as a resource for researchers and practitioners looking to understand and integrate differentiable physics within their research. We conclude by highlighting current limitations as well as providing insights into future directions for the field.