92 citations · 280 across the 15 of their papers we have counts for
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Observable functions of rational ODE models and how to find them
Alexander Demin, Gleb Pogudin, Christopher Rackauckas
Consider a parametric ODE control model. A function of the states and parameters is called observable if its value can in principle be reconstructed from input-output data. The obs…
ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models
Kristian Meyer, Maksym Ratajczyk, Christopher Rackauckas
Mechanistic chromatography models are valuable for process development, but gradient-based parameter estimation and optimization can be hindered by computational cost and the effor…
Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards
Pengfei Cai, Utkarsh Utkarsh, Alan Edelman +2
Partial differential equations (PDEs) are foundational to modeling in science and engineering, but constructing reliable numerical solvers remains labor-intensive, demanding expert…
SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling
Alaina Kolli, Theodoros Xenakis, Utkarsh Utkarsh +4
Generative models have emerged as scalable surrogates for physical simulation, yet they offer no guarantee that their outputs respect the conservation laws, boundary conditions, an…