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
Tomas Skrivan developed a formal variational calculus framework for the HEPLean/PhysLean repository, focusing on rigorous mathematical modeling and optimization in Lean. He introduced core abstractions such as HasVarAdjoint, HasVarGradient, and adjFDeriv, along with supporting lemmas and proofs to ensure correctness, extensionality, and uniqueness. His work included a major refactor to unify adjoint properties and new definitions for Euler-Lagrange and Hamilton’s equations, enabling robust variational analysis in physics-informed workflows. Leveraging skills in formal verification, functional programming, and type theory, Tomas delivered well-documented, incremental improvements that strengthened the mathematical foundation for future optimization and control applications.
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
132 people
Work 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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