Add Chris Rackauckas publications from 2023-2025

[ChrisRackauckas-Claude]

Summary

This PR adds five recent publications co-authored by Chris Rackauckas to the JuliaLab bibliography, following the same approach as PR #72.

Publications Added

1. Efficient Symbolic Computation via Hash Consing (arXiv:2509.20534, 2025)

   • Presents the first integration of hash consing into JuliaSymbolics
   • Reduces memory consumption and accelerates symbolic operations
   • Authors: Zhu, Sabharwal, Tan, Ma, Edelman, Rackauckas

2. The Future of Artificial Intelligence and the Mathematical and Physical Sciences (arXiv:2509.02661, 2025)

   • Community paper from NSF Future of AI+MPS Workshop
   • 100+ contributors including Christopher Rackauckas (JuliaHub, Pumas-AI, MIT)
   • Proposes strategic priorities for bidirectional AI+MPS research

3. Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints (arXiv:2506.04171, 2025)

   • Zero-shot inference framework that enforces arbitrary nonlinear constraints
   • Authors: Utkarsh, Cai, Edelman, Gomez-Bombarelli, Rackauckas

4. Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints (arXiv:2505.20515, 2025)

   • Method for enforcing algebraic constraints in neural differential equations
   • Achieves constraint violation errors below 10^{-10}
   • Authors: Pal, Edelman, Rackauckas

5. Robust Parameter Estimation for Rational Ordinary Differential Equations (arXiv:2303.02159, 2023/2026)

   • Published in Applied Mathematics and Computation, Vol. 509, January 2026
   • Novel approach using differential algebra and polynomial system solving
   • Authors: Bassik, Berman, Go, Hong, Ilmer, Ovchinnikov, Rackauckas, Soto, Yap

Changes Made

• Added BibTeX entries to _assets/julialab.bib in chronological order
• All entries include required fields: title, author, year, url
• Added abstracts for context
• Followed existing formatting conventions from the file

Impact

These additions expand the JuliaLab bibliography with recent work in:

• Scientific machine learning
• Symbolic computation optimization
• Neural differential equations
• Generative models with physical constraints
• Parameter estimation for differential equations

🤖 Generated with Claude Code