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Differentiable Analog Quantum Computing for Optimization and Control

We formulate the first differentiable analog quantum computing framework with specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable …

NeuPhysics - Editable Neural Geometry and Physics from Monocular Videos

We present a method for learning geometry and physics parameters of a dynamic scene requiring only a monocular RGB video. Our approach uses a hybrid representation of neural fields and hexahedra mesh, enabling objects in the scene to be interactively …

Differentiable Hybrid Traffic Simulation

We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This …

Differentiable Simulation of Soft Multi-body Systems

We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective …

Efficient Differentiable Simulation of Articulated Bodies

We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks, and gradient-based optimization of neural networks that operate on articulated …

OF-VO Efficient Navigation among Pedestrians Using Commodity Sensors

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D …

Differentiable Fluids with Solid Coupling for Learning and Control

We introduce an efficient differentiable fluid simulator that can be integrated with deep neural networks as a part of layers for learning dynamics and solving control problems. It offers the capability to handle one-way coupling of fluids with rigid …

Scalable Differentiable Physics for Learning and Control

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilities of differentiable physics solvers remain limited. We develop a …

Learning-based Intrinsic Reflectional Symmetry Detection

Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this paper, we propose a learning-based approach to intrinsic …

Learning on 3D Meshes with Laplacian Encoding and Pooling

3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, …