OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing

2026-04-08

AI Summary

This work addresses the lack of a unified, reproducible framework for developing and evaluating physical reservoir computing (PRC). To this end, we propose OpenPRC, an open-source, end-to-end Python framework that, for the first time, seamlessly integrates GPU-accelerated simulations (e.g., via the demlat engine) with real-world video experimental data through a standardized HDF5-based data architecture. OpenPRC supports comprehensive analysis from physical dynamics to task performance by incorporating physics-aware optimizers, information-theoretic diagnostic tools, and modular learning layers. The framework is compatible with external simulation engines such as PyBullet and has been validated on tasks including origami-based systems. It provides standardized benchmarks, capacity analyses, and correlation diagnostics, thereby promoting rigor, standardization, and reproducibility in PRC research.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Physical Reservoir Computing (PRC) leverages the intrinsic nonlinear dynamics of physical substrates, mechanical, optical, spintronic, and beyond, as fixed computational reservoirs, offering a compelling paradigm for energy-efficient and embodied machine learning. However, the practical workflow for developing and evaluating PRC systems remains fragmented: existing tools typically address only isolated parts of the pipeline, such as substrate-specific simulation, digital reservoir benchmarking, or readout training. What is missing is a unified framework that can represent both high-fidelity simulated trajectories and real experimental measurements through the same data interface, enabling reproducible evaluation, analysis, and physics-aware optimization across substrates and data sources. We present OpenPRC, an open-source Python framework that fills this gap through a schema-driven physics-to-task pipeline built around five modules: a GPU-accelerated hybrid RK4-PBD physics engine (demlat), a video-based experimental ingestion layer (openprc.vision), a modular learning layer (reservoir), information-theoretic analysis and benchmarking tools (analysis), and physics-aware optimization (optimize). A universal HDF5 schema enforces reproducibility and interoperability, allowing GPU-simulated and experimentally acquired trajectories to enter the same downstream workflow without modification. Demonstrated capabilities include simulations of Origami tessellations, video-based trajectory extraction from a physical reservoir, and a common interface for standardized PRC benchmarking, correlation diagnostics, and capacity analysis. The longer-term vision is to serve as a standardizing layer for the PRC community, compatible with external physics engines including PyBullet, PyElastica, and MERLIN.

Problem

• Physical Reservoir Computing
• unified framework
• reproducible evaluation
• physics-to-task pipeline
• interoperability

Innovation

• Physical Reservoir Computing
• Unified Framework
• Physics-to-Task Pipeline
• GPU-Accelerated Simulation
• Reproducible Evaluation