HEPLean/PhysLean
Jun 2025 – Jul 2025
2 Months active
Languages Used
Lean
Technical Skills
Abstract MathematicsCalculusCalculus of VariationsFormal VerificationFunctional AnalysisFunctional Programming
Tomáš Skřivan
PROFILE
Tomáš Skřivan
Developed and expanded a formal variational calculus framework within the PhysLean repository, focusing on rigorous mathematical modeling for physics-informed optimization. Leveraging Lean and advanced concepts from calculus of variations, differential geometry, and formal verification, the work introduced abstractions for variational adjoints, gradients, and derivatives, along with supporting lemmas and proofs to ensure correctness and uniqueness. The framework was incrementally documented and refactored to include unified definitions for Euler-Lagrange and Hamilton’s equations, strengthening the mathematical foundation for variational analysis. These contributions enable robust optimization workflows and lay the groundwork for future applications in control and data-driven modeling within PhysLean.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
2
Lines of code
4,941
Activity Months2
Your Network
148 peopleWork History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07): Delivered a major refactor and expansion of the variational adjoint calculus framework in HEPLean/PhysLean, introducing adjoint properties and new definitions for Euler-Lagrange and Hamilton's equations, with comprehensive proofs and lemmas. This work strengthens the library's mathematical backbone, enabling more robust variational analyses and optimization workflows in physics-informed modeling. The effort lays the groundwork for future optimization, control, and data-driven applications.
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07): Delivered a major refactor and expansion of the variational adjoint calculus framework in HEPLean/PhysLean, introducing adjoint properties and new definitions for Euler-Lagrange and Hamilton's equations, with comprehensive proofs and lemmas. This work strengthens the library's mathematical backbone, enabling more robust variational analyses and optimization workflows in physics-informed modeling. The effort lays the groundwork for future optimization, control, and data-driven applications.
July 2025
June 2025
7 Commits • 1 Features
Jun 1, 2025June 2025: Implemented a formal variational calculus framework in PhysLean, introducing HasVarAdjoint, HasVarGradient, HasVarAdjDerivAt, and adjFDeriv, with supporting lemmas, extensionality, uniqueness, and an example usage. This delivers rigorous variational analysis and gradient-based optimization capabilities, ready for integration into optimization pipelines.
June 2025
7 Commits • 1 Features
Jun 1, 2025June 2025: Implemented a formal variational calculus framework in PhysLean, introducing HasVarAdjoint, HasVarGradient, HasVarAdjDerivAt, and adjFDeriv, with supporting lemmas, extensionality, uniqueness, and an example usage. This delivers rigorous variational analysis and gradient-based optimization capabilities, ready for integration into optimization pipelines.
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Quality Metrics
Correctness98.8%
Maintainability92.4%
Architecture96.4%
Performance77.6%
AI Usage35.0%
Skills & Technologies
Programming Languages
Lean
Technical Skills
Abstract MathematicsCalculusCalculus of VariationsDifferential GeometryFormal VerificationFunctional AnalysisFunctional ProgrammingLean Theorem ProverLinear AlgebraMathematical ProofMathematical ProofsType TheoryVariational Calculus
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