google-deepmind/torax
Nov 2024 – Jun 2026
20 Months active
Languages Used
C++JAXJinjaPythonRSTTOMLYAMLreStructuredText
Technical Skills
Backend DevelopmentBug FixingCI/CD ConfigurationCode DocumentationCode RefactoringConfiguration Management
Jonathan Citrin
PROFILE
Jonathan Citrin
Over 20 months, contributed to the google-deepmind/torax repository by building and refining advanced plasma physics simulation capabilities. Work focused on modularizing core and edge physics models, enhancing impurity and transport modeling, and improving simulation stability through robust validation and adaptive algorithms. Leveraged Python, JAX, and Pydantic to implement configurable APIs, high-performance numerics, and structured data models. Delivered features such as stateful pedestal evaluation, multi-model heating sources, and edge/divertor integration, while maintaining rigorous test coverage and documentation. Addressed complex numerical and physical challenges, resulting in more accurate, maintainable, and scalable simulations for fusion energy research and development workflows.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
390Total
Bugs
62
Commits
390
Features
150
Lines of code
79,649
Activity Months20
Your Network
5048 peopleSame Organization
@google.com5001
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Shared Repositories
47
Work History
June 2026
13 Commits • 4 Features
Jun 1, 2026June 2026 monthly performance summary for google-deepmind/torax: Delivered substantial pedestal model enhancements, stability improvements, and targeted robustness fixes, culminating in a TORAX 1.4.2 release. These changes improve solver reliability, reduce unnecessary computations, and tighten impurity handling, directly contributing to more accurate transport predictions and more stable L-H transitions. The work emphasizes stateful pedestal evaluation, robustness in impurity handling, and configuration-driven controls that reduce nonlinearity and drift in long-running simulations. This period also included performance-oriented utilities and documentation updates to support maintainability and reproducibility.
13 Commits • 4 Features
Jun 1, 2026June 2026 monthly performance summary for google-deepmind/torax: Delivered substantial pedestal model enhancements, stability improvements, and targeted robustness fixes, culminating in a TORAX 1.4.2 release. These changes improve solver reliability, reduce unnecessary computations, and tighten impurity handling, directly contributing to more accurate transport predictions and more stable L-H transitions. The work emphasizes stateful pedestal evaluation, robustness in impurity handling, and configuration-driven controls that reduce nonlinearity and drift in long-running simulations. This period also included performance-oriented utilities and documentation updates to support maintainability and reproducibility.
June 2026
May 2026
18 Commits • 6 Features
May 1, 2026May 2026 delivered substantial stability, realism, and reliability improvements for the Torax project. Key work spanned transport stability, flexible heating modeling, ML robustness, and test infrastructure, all aimed at increasing predictive fidelity and reducing operational risk in simulations across varying magnetic field conditions.
May 2026
18 Commits • 6 Features
May 1, 2026May 2026 delivered substantial stability, realism, and reliability improvements for the Torax project. Key work spanned transport stability, flexible heating modeling, ML robustness, and test infrastructure, all aimed at increasing predictive fidelity and reducing operational risk in simulations across varying magnetic field conditions.
April 2026
9 Commits • 3 Features
Apr 1, 2026In April 2026, delivered a focused set of pedestal, geometry, robustness, and model-architecture enhancements for google-deepmind/torax, with a strong emphasis on accuracy, reliability, and maintainability. Key features include pedestal transition modeling enhancements with adaptive source mode, refined interpolation between L- and H-mode values, and a hysteresis factor to stabilize H-L transitions; clarified B_0 definition in Torax geometry and updated the eqdsk loader with regenerated tests to improve accuracy of vacuum field calculations. Major bugs fixed include an explicit cast for PedestalTransitionState.confinement_mode to prevent double compilation and ensure correct state handling. Robustness improvements add guards against division-by-zero in JAX impurity calculations, a warning for coarse equilibrium grids, and expanded path_utils test coverage. A refactor of the ion_cyclotron_source now supports multiple models (including toric_nn) via a new subpackage structure, enabling easier experimentation with alternative source models. Overall impact: higher-fidelity pedestal dynamics, more reliable numerical behavior, and a scalable foundation for future model experimentation, delivering business value through more accurate simulations, reduced troubleshooting time, and improved test coverage. Technologies/skills demonstrated: numerical modeling with adaptive control flows, JAX-aware safeguards, Python software engineering, test-driven development, code architecture for modular model options, and geometry/physics data handling (eqdsk, B_0 calculations).
9 Commits • 3 Features
Apr 1, 2026In April 2026, delivered a focused set of pedestal, geometry, robustness, and model-architecture enhancements for google-deepmind/torax, with a strong emphasis on accuracy, reliability, and maintainability. Key features include pedestal transition modeling enhancements with adaptive source mode, refined interpolation between L- and H-mode values, and a hysteresis factor to stabilize H-L transitions; clarified B_0 definition in Torax geometry and updated the eqdsk loader with regenerated tests to improve accuracy of vacuum field calculations. Major bugs fixed include an explicit cast for PedestalTransitionState.confinement_mode to prevent double compilation and ensure correct state handling. Robustness improvements add guards against division-by-zero in JAX impurity calculations, a warning for coarse equilibrium grids, and expanded path_utils test coverage. A refactor of the ion_cyclotron_source now supports multiple models (including toric_nn) via a new subpackage structure, enabling easier experimentation with alternative source models. Overall impact: higher-fidelity pedestal dynamics, more reliable numerical behavior, and a scalable foundation for future model experimentation, delivering business value through more accurate simulations, reduced troubleshooting time, and improved test coverage. Technologies/skills demonstrated: numerical modeling with adaptive control flows, JAX-aware safeguards, Python software engineering, test-driven development, code architecture for modular model options, and geometry/physics data handling (eqdsk, B_0 calculations).
April 2026
March 2026
14 Commits • 5 Features
Mar 1, 2026For 2026-03, the developer delivered substantial improvements in physics modeling, stability, and performance across the google-deepmind/torax repository. Highlights include parallel advances in radiation modeling (Wesson and Mavrin with an impurity-exclusion option to prevent double counting), fast ion stabilization integrated into transport, adaptive pedestal handling with ramp-scaling enabled by a simulation-time parameter, enhanced boundary conditions for electron density (including a density_fraction mode with safe initialization logic), and a default divertor configuration update from VT to HT that alters H-mode transition power calculations. Core fixes improved robustness: zero cooling rate for hydrogenic ions to prevent crashes, centralized sign conventions for psi and Ip_profile, and corrected flux-surface-averaged grad psi with the missing 2*pi factor. These changes were accompanied by test improvements (e.g., new tests for sign flipping, Cauchy-Schwarz inequality, and density_fraction behavior) and documentation updates. Collectively, these work items increase model fidelity, reduce runtime failures, and deliver measurable business value through more accurate predictions and stable simulations across production and development environments.
March 2026
14 Commits • 5 Features
Mar 1, 2026For 2026-03, the developer delivered substantial improvements in physics modeling, stability, and performance across the google-deepmind/torax repository. Highlights include parallel advances in radiation modeling (Wesson and Mavrin with an impurity-exclusion option to prevent double counting), fast ion stabilization integrated into transport, adaptive pedestal handling with ramp-scaling enabled by a simulation-time parameter, enhanced boundary conditions for electron density (including a density_fraction mode with safe initialization logic), and a default divertor configuration update from VT to HT that alters H-mode transition power calculations. Core fixes improved robustness: zero cooling rate for hydrogenic ions to prevent crashes, centralized sign conventions for psi and Ip_profile, and corrected flux-surface-averaged grad psi with the missing 2*pi factor. These changes were accompanied by test improvements (e.g., new tests for sign flipping, Cauchy-Schwarz inequality, and density_fraction behavior) and documentation updates. Collectively, these work items increase model fidelity, reduce runtime failures, and deliver measurable business value through more accurate predictions and stable simulations across production and development environments.
February 2026
12 Commits • 3 Features
Feb 1, 2026February 2026 focused on delivering reliable, scalable Lengyel solver capabilities and data quality improvements in torax. Key outcomes include a multistart extended Lengyel forward-mode solver that robustly finds roots and reports multiple valid solutions; enabling solver seeding from previous outputs and introducing InitialGuessConfig to control convergence; hardening residuals and safeguarding against bad initial guesses to reduce spurious results; fixing seed impurity outputs by isolating seed dimension and eliminating NaNs; and improving geometry data initialization accuracy through epsilon update, enhancing numerical fidelity for downstream analyses. These changes improve model reliability, reduce manual debugging, and accelerate data-ready results for downstream workflows.
12 Commits • 3 Features
Feb 1, 2026February 2026 focused on delivering reliable, scalable Lengyel solver capabilities and data quality improvements in torax. Key outcomes include a multistart extended Lengyel forward-mode solver that robustly finds roots and reports multiple valid solutions; enabling solver seeding from previous outputs and introducing InitialGuessConfig to control convergence; hardening residuals and safeguarding against bad initial guesses to reduce spurious results; fixing seed impurity outputs by isolating seed dimension and eliminating NaNs; and improving geometry data initialization accuracy through epsilon update, enhancing numerical fidelity for downstream analyses. These changes improve model reliability, reduce manual debugging, and accelerate data-ready results for downstream workflows.
February 2026
January 2026
22 Commits • 16 Features
Jan 1, 2026January 2026 monthly summary for google-deepmind/torax. Delivered stability, configurability, and modeling flexibility across numerics, transport models, and post-processing. Notable API and data-model enhancements, plus extensive test coverage and documentation improvements.
January 2026
22 Commits • 16 Features
Jan 1, 2026January 2026 monthly summary for google-deepmind/torax. Delivered stability, configurability, and modeling flexibility across numerics, transport models, and post-processing. Notable API and data-model enhancements, plus extensive test coverage and documentation improvements.
December 2025
18 Commits • 2 Features
Dec 1, 2025Monthly summary for 2025-12 for google-deepmind/torax focusing on delivering edge/divertor physics integration and robustness improvements, with emphasis on business value and technical achievements.
18 Commits • 2 Features
Dec 1, 2025Monthly summary for 2025-12 for google-deepmind/torax focusing on delivering edge/divertor physics integration and robustness improvements, with emphasis on business value and technical achievements.
December 2025
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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).
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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Quality Metrics
Correctness94.2%
Maintainability90.6%
Architecture90.8%
Performance83.4%
AI Usage22.4%
Skills & Technologies
Programming Languages
C++JAXJSONJinjaMarkdownNetCDFNumPyPythonRSTRst
Technical Skills
API DesignAPI MaintenanceAPI UsageAPI designAbstractionBackend DevelopmentBackward CompatibilityBug FixBug FixingCI/CDCI/CD ConfigurationClass DesignCode ClarityCode CleanupCode Consistency
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