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JAXSR Documentation

Getting Started

Quickstart Guide

Guides

Additive Symbolic Regression
Design of Experiments with JAXSR
Active Learning & Acquisition Functions
JAXSR Command-Line Interface Guide
Claude Code Skills
Performance Guide: Making JAXSR Fast on CPU and GPU
Resampling and Selection Stability
Sample Weights
Scikit-learn Integration
Structured Basis Blocks
Superposition: Learning a Master Curve’s Shift Law
Multivariate Derivative Estimation

Examples

Basic Usage
Additive Symbolic Regression
Chemical Kinetics
Heat Transfer
Uncertainty Quantification
Active Learning
Categorical Variables
DOE Study Workflow
JAXSR: Comprehensive Tutorial
Multi-Level Constraint Enforcement in JAXSR
Constrained Regression: Sin Wave with x-Dependent Output Bounds
Thermodynamic Consistency: Activity Coefficients with the Gibbs-Duhem Constraint
Langmuir Isotherm: DOE-Driven Active Learning
Model Comparison: Langmuir vs Freundlich Isotherms
Response Surface Methodology: Formulation Optimization
Serialization & Sharing: Save, Load, Export, Reproduce
ODE Discovery with SINDy-style Sparse Regression
Time–Temperature Superposition: Learning the Shift Law
Symbolic Classification
JAXSR Performance: CPU vs GPU/MPS Benchmarking
CLI Workflow: Catalyst Screening Study

Reference

API Reference

Background

Literature Review: Symbolic Regression Methods
Comparison: ALAMO vs SISSO vs JAXSR

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By John Kitchin

© Copyright 2026 John Kitchin.