google-deepmind/torax
Nov 2024 – Nov 2025
13 Months active
Languages Used
C++JAXJinjaPythonRSTTOMLYAMLreStructuredText
Technical Skills
Backend DevelopmentBug FixingCI/CD ConfigurationCode DocumentationCode RefactoringConfiguration Management
Jonathan Citrin
PROFILE
Jonathan Citrin
Over thirteen months, Citrin led core development of the google-deepmind/torax repository, advancing physics simulation fidelity and maintainability. He architected and refactored major modules, including the sawtooth and Lengyel edge-physics solvers, and introduced Pydantic-based configuration for safer, more flexible experimentation. His work integrated advanced impurity modeling, neoclassical transport, and robust post-processing, leveraging Python, JAX, and Pydantic for high-performance, type-safe computation. By generalizing APIs, improving test reliability, and modernizing data structures, Citrin enabled scalable, reproducible plasma simulations. The depth of engineering is reflected in modular abstractions, rigorous validation, and a focus on maintainable, extensible scientific software for fusion research.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
284Total
Bugs
48
Commits
284
Features
111
Lines of code
60,138
Activity Months13
Your Network
4192 people
Work History
November 2025
1 Commits • 1 Features
Nov 1, 2025In November 2025, delivered a major configurability enhancement for the Lengyel model in google-deepmind/torax. Introduced Pydantic-based configuration and runtime parameters to replace ad-hoc configs with structured validation, expanding the parameter surface and improving reliability for experiments. The refactor updates default values and renames several parameters for clarity, setting a foundation for safer, faster experimentation and easier onboarding for new contributors. This work reduces configuration errors, accelerates iterative testing, and enables scalable parameter exploration across experiments.
1 Commits • 1 Features
Nov 1, 2025In November 2025, delivered a major configurability enhancement for the Lengyel model in google-deepmind/torax. Introduced Pydantic-based configuration and runtime parameters to replace ad-hoc configs with structured validation, expanding the parameter surface and improving reliability for experiments. The refactor updates default values and renames several parameters for clarity, setting a foundation for safer, faster experimentation and easier onboarding for new contributors. This work reduces configuration errors, accelerates iterative testing, and enables scalable parameter exploration across experiments.
November 2025
October 2025
14 Commits • 3 Features
Oct 1, 2025In 2025-10, delivered a comprehensive modernization of the Extended Lengyel solver in google-deepmind/torax, established a foundational edge-physics modeling framework, and strengthened testing/documentation reliability. The work enhances solver stability, performance, and maintainability, enabling TORAX edge simulations to be more robust and feature-ready for the roadmap ahead.
October 2025
14 Commits • 3 Features
Oct 1, 2025In 2025-10, delivered a comprehensive modernization of the Extended Lengyel solver in google-deepmind/torax, established a foundational edge-physics modeling framework, and strengthened testing/documentation reliability. The work enhances solver stability, performance, and maintainability, enabling TORAX edge simulations to be more robust and feature-ready for the roadmap ahead.
September 2025
35 Commits • 9 Features
Sep 1, 2025September 2025 monthly summary for google-deepmind/torax. Delivered major features, fixed critical bugs, and advanced modeling capabilities while improving test reliability and documentation. Highlights include public I/O and versioning workflow, edge physics and Lengyel model enhancements, and forward/inverse solver capabilities.
35 Commits • 9 Features
Sep 1, 2025September 2025 monthly summary for google-deepmind/torax. Delivered major features, fixed critical bugs, and advanced modeling capabilities while improving test reliability and documentation. Highlights include public I/O and versioning workflow, edge physics and Lengyel model enhancements, and forward/inverse solver capabilities.
September 2025
August 2025
22 Commits • 10 Features
Aug 1, 2025August 2025: Delivered substantial impurity handling improvements and performance optimizations in torax. Implemented advanced impurity input modes (n_e_ratio, n_e_ratios, Z_eff constraints) with zero-impurity support, generalized impurity framework and profiles (fractions, validation), and dynamic runtime enhancements (dynamic naming, JIT). Fixed correctness issues in charge state computations and references, improved impurity calculations (vectorization) and psi initialization practices. Result: more accurate impurity modeling, faster runtimes, and improved code quality and maintainability.
August 2025
22 Commits • 10 Features
Aug 1, 2025August 2025: Delivered substantial impurity handling improvements and performance optimizations in torax. Implemented advanced impurity input modes (n_e_ratio, n_e_ratios, Z_eff constraints) with zero-impurity support, generalized impurity framework and profiles (fractions, validation), and dynamic runtime enhancements (dynamic naming, JIT). Fixed correctness issues in charge state computations and references, improved impurity calculations (vectorization) and psi initialization practices. Result: more accurate impurity modeling, faster runtimes, and improved code quality and maintainability.
July 2025
8 Commits • 3 Features
Jul 1, 2025July 2025 (google-deepmind/torax): Key features delivered include refactoring neoclassical transport models into a dedicated formulas module with epsilon as a geometry property and adding helper functions for Sauter bootstrap coefficients (L31, L32, L34, alpha), plus integrating the Angioni-Sauter transport model and updating the default to angioni_sauter. Major bugs fixed include ensuring n_rho updates when the entire geometry dictionary is passed to update_fields, triggering mesh rebuild and cache clearance, supported by a new test. Overall impact: improved physical fidelity, stability, and maintainability of Torax simulations; default baseline now reflects current physics, enabling more reliable mission planning and comparisons, and groundwork laid for impurity density fractions. Technologies/skills demonstrated: Python modular refactor, module-level formulas abstraction, numerical modeling of bootstrap coefficients, test-driven validation, cache/mesh management, and impurity modeling improvements enabling future density fraction work.
8 Commits • 3 Features
Jul 1, 2025July 2025 (google-deepmind/torax): Key features delivered include refactoring neoclassical transport models into a dedicated formulas module with epsilon as a geometry property and adding helper functions for Sauter bootstrap coefficients (L31, L32, L34, alpha), plus integrating the Angioni-Sauter transport model and updating the default to angioni_sauter. Major bugs fixed include ensuring n_rho updates when the entire geometry dictionary is passed to update_fields, triggering mesh rebuild and cache clearance, supported by a new test. Overall impact: improved physical fidelity, stability, and maintainability of Torax simulations; default baseline now reflects current physics, enabling more reliable mission planning and comparisons, and groundwork laid for impurity density fractions. Technologies/skills demonstrated: Python modular refactor, module-level formulas abstraction, numerical modeling of bootstrap coefficients, test-driven validation, cache/mesh management, and impurity modeling improvements enabling future density fraction work.
July 2025
June 2025
42 Commits • 12 Features
Jun 1, 2025June 2025 monthly summary for google-deepmind/torax. The focus was on increasing physics accuracy, data quality, and maintainability. Key features delivered included Core profiles outputs and Z_eff integration (generalized outputs and replacement of Z_eff references with CoreProfiles), validation and data structures improvements (new Ion dataclass and extended NaN checks), post-processing enhancements (integrated particle sources included in post-processing and scalar outputs; removed unused _trapz), numerics and redistribution updates (W_thermal now uses math_utils.volume_integration; sawtooth redistribution preserves dW/dt from last timestep), and TORAX enhancements (scalar beta outputs added and Pydantic-based transport parameter validation to improve reliability).
June 2025
42 Commits • 12 Features
Jun 1, 2025June 2025 monthly summary for google-deepmind/torax. The focus was on increasing physics accuracy, data quality, and maintainability. Key features delivered included Core profiles outputs and Z_eff integration (generalized outputs and replacement of Z_eff references with CoreProfiles), validation and data structures improvements (new Ion dataclass and extended NaN checks), post-processing enhancements (integrated particle sources included in post-processing and scalar outputs; removed unused _trapz), numerics and redistribution updates (W_thermal now uses math_utils.volume_integration; sawtooth redistribution preserves dW/dt from last timestep), and TORAX enhancements (scalar beta outputs added and Pydantic-based transport parameter validation to improve reliability).
May 2025
56 Commits • 22 Features
May 1, 2025May 2025 (2025-05) monthly summary for google-deepmind/torax. Focused on delivering a stable, solver-based Sawtooth core, improving runtime consistency, documentation, and release-readiness, while tightening tests and fixing critical data-path bugs. The month culminated in a first-class foundation for future releases with improved observability and configurable API consistency.
56 Commits • 22 Features
May 1, 2025May 2025 (2025-05) monthly summary for google-deepmind/torax. Focused on delivering a stable, solver-based Sawtooth core, improving runtime consistency, documentation, and release-readiness, while tightening tests and fixing critical data-path bugs. The month culminated in a first-class foundation for future releases with improved observability and configurable API consistency.
May 2025
April 2025
10 Commits • 4 Features
Apr 1, 2025April 2025 monthly summary for google-deepmind/torax: Focused on delivering v0.3.1-ready tutorials/docs, expanding physics modeling with energy-conserving profile flattening, and stabilizing sawtooth-related dynamics, with caching and test improvements to boost reliability and performance. Key outcomes include new tutorials and docs alignment, generalized flattening utilities for density and temperature, a flatten_current_profile function for sawtooth simulations, a first-pass sawtooth redistribution model with hashing/equality for caching, crash safety improvements, and test fixes to ensure compatibility with evolving outputs.
April 2025
10 Commits • 4 Features
Apr 1, 2025April 2025 monthly summary for google-deepmind/torax: Focused on delivering v0.3.1-ready tutorials/docs, expanding physics modeling with energy-conserving profile flattening, and stabilizing sawtooth-related dynamics, with caching and test improvements to boost reliability and performance. Key outcomes include new tutorials and docs alignment, generalized flattening utilities for density and temperature, a flatten_current_profile function for sawtooth simulations, a first-pass sawtooth redistribution model with hashing/equality for caching, crash safety improvements, and test fixes to ensure compatibility with evolving outputs.
March 2025
27 Commits • 8 Features
Mar 1, 2025March 2025 performance highlights for google-deepmind/torax. This month concentrated on delivering core physics improvements, expanding diagnostics capabilities, stabilizing the test suite, and advancing integration with the TORAX workflow. Key refactors and feature work establish a stronger foundation for reliability, maintainability, and end-to-end modeling through the sawtooth pathway, while preserving accuracy in physics calculations.
27 Commits • 8 Features
Mar 1, 2025March 2025 performance highlights for google-deepmind/torax. This month concentrated on delivering core physics improvements, expanding diagnostics capabilities, stabilizing the test suite, and advancing integration with the TORAX workflow. Key refactors and feature work establish a stronger foundation for reliability, maintainability, and end-to-end modeling through the sawtooth pathway, while preserving accuracy in physics calculations.
March 2025
February 2025
25 Commits • 16 Features
Feb 1, 2025February 2025 monthly summary for google-deepmind/torax. Delivered a set of features that enhance simulation fidelity, data visibility, and developer experience, while making targeted robustness improvements. Key features delivered include pedestal model enablement in build_sim, geometry outputs extended to include Geometry properties, passing total Ip to psi gradient constraints, inclusion of Li3 and Wpol in post_processed_outputs, and a simulation progress bar with reduced verbosity.
February 2025
25 Commits • 16 Features
Feb 1, 2025February 2025 monthly summary for google-deepmind/torax. Delivered a set of features that enhance simulation fidelity, data visibility, and developer experience, while making targeted robustness improvements. Key features delivered include pedestal model enablement in build_sim, geometry outputs extended to include Geometry properties, passing total Ip to psi gradient constraints, inclusion of Li3 and Wpol in post_processed_outputs, and a simulation progress bar with reduced verbosity.
January 2025
16 Commits • 5 Features
Jan 1, 20252025-01 TORAX monthly summary — This period focused on increasing physics fidelity, configurability, and reliability to accelerate design cycles and reduce risk. Major features delivered include IonMixture-based generalized dilution with API and validation plus incorporation of actual D/T densities into fusion power calculations; Cyclotron radiation heat sink integration with accompanying documentation and plotting support; Impurity radiation heat sink module enhancements with a polynomial-fit model; Post-processing updated to include H89P L-mode confinement scaling and revised confinement time calculations. Supporting improvements included codebase refactors for plotting robustness and grid-based sources, plus a robust test stability fix to avoid division-by-zero in simulation comparisons. Business impact: more accurate simulations, easier configuration of plasma composition, improved maintainability, faster validation, and reduced risk in DT scenarios.
16 Commits • 5 Features
Jan 1, 20252025-01 TORAX monthly summary — This period focused on increasing physics fidelity, configurability, and reliability to accelerate design cycles and reduce risk. Major features delivered include IonMixture-based generalized dilution with API and validation plus incorporation of actual D/T densities into fusion power calculations; Cyclotron radiation heat sink integration with accompanying documentation and plotting support; Impurity radiation heat sink module enhancements with a polynomial-fit model; Post-processing updated to include H89P L-mode confinement scaling and revised confinement time calculations. Supporting improvements included codebase refactors for plotting robustness and grid-based sources, plus a robust test stability fix to avoid division-by-zero in simulation comparisons. Business impact: more accurate simulations, easier configuration of plasma composition, improved maintainability, faster validation, and reduced risk in DT scenarios.
January 2025
December 2024
4 Commits • 2 Features
Dec 1, 2024December 2024: Google-DeepMind torax updates focused on test reliability and runtime configurability. Key changes include cleaning up code/docs in update_sim geometry provider and refactoring ToricNNWrapper tests to a classmethod for consistency, and introducing a static torax_mesh parameter with updates to hashing/build flow. The gyrbohm function now accepts explicit reference values for temperature, magnetic field, and mass, with corresponding updates to calculate_chiGB and calculate_alpha. These changes improve test durability, reproducibility, and runtime performance while enabling better parameter control and build integrity.
December 2024
4 Commits • 2 Features
Dec 1, 2024December 2024: Google-DeepMind torax updates focused on test reliability and runtime configurability. Key changes include cleaning up code/docs in update_sim geometry provider and refactoring ToricNNWrapper tests to a classmethod for consistency, and introducing a static torax_mesh parameter with updates to hashing/build flow. The gyrbohm function now accepts explicit reference values for temperature, magnetic field, and mass, with corresponding updates to calculate_chiGB and calculate_alpha. These changes improve test durability, reproducibility, and runtime performance while enabling better parameter control and build integrity.
November 2024
24 Commits • 16 Features
Nov 1, 2024During 2024-11, google-deepmind/torax delivered major improvements to physics fidelity, maintainability, and usability across the torax repository. Key features include refactoring the quasilinear model for better structure and maintainability, generalizing the core transport construction, and extending impurity handling in ion–electron collisional exchange. Post-processing was enhanced with SOL power naming and LH transition powers. The work also adds confinement quality factors and strengthens type safety and tests, alongside documentation updates.
24 Commits • 16 Features
Nov 1, 2024During 2024-11, google-deepmind/torax delivered major improvements to physics fidelity, maintainability, and usability across the torax repository. Key features include refactoring the quasilinear model for better structure and maintainability, generalizing the core transport construction, and extending impurity handling in ion–electron collisional exchange. Post-processing was enhanced with SOL power naming and LH transition powers. The work also adds confinement quality factors and strengthens type safety and tests, alongside documentation updates.
November 2024
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Quality Metrics
Correctness93.4%
Maintainability91.6%
Architecture90.6%
Performance81.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++JAXJSONJinjaMarkdownNetCDFNumPyPythonRSTRst
Technical Skills
API DesignAPI MaintenanceAPI UsageAbstractionBackend DevelopmentBackward CompatibilityBug FixBug FixingCI/CDCI/CD ConfigurationClass DesignCode ClarityCode CleanupCode ConsistencyCode Documentation
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