Author

Christopher Rackauckas

h-index 21 View on Semantic Scholar

We have 5 of 68 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access 2026

DeepNLME - Conditional Generative Scientific Machine Learning with Applications in Pharmacology

Nonlinear mixed-effects (NLME) models are essential for analyzing hierarchical and longitudinal data, particularly in pharmacometrics. This paper presents deep nonlinear mixed effects (DeepNLME) models and applications thereof in the DeepPumas software framework. DeepNLME integrates neural networks with scientific an...

Niklas Korsbo, M. Tarek, Lorenzo Contento et al. · Quantitative Medicine · 0 citations

Preprint Sep 2026

Observable functions of rational ODE models and how to find them

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 observable functions form a field, called the observation field, represented naturally by a set of generators. Even when the model is not fully...

Alexander Demin, Gleb Pogudin, Christopher Rackauckas · 1 citation

#artificial intelligence Jun 2026

SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling

This work exploits the structure that sample-wise batching and local PDE couplings induce in the projection subproblems, exposing this structure using ExaModels.jl and GPU sparse factorization to show that sparse GPU nonlinear optimization is a practical foundation for constrained generative sampling in scientific mach...

Alaina Kolli, Theodoros G. Xenakis, Utkarsh Utkarsh et al. · arXiv.org · 0 citations

Jul 2026

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards

This work introduces RLVP: Reinforcement Learning with Verifiable Physics, an RL post-training framework for multi-PDE solver code generation and shows that a smaller LLM post-trained with RLVP can outperform prompting a frontier model on in-distribution PDE solver generation.

Pengfei Cai, Utkarsh Utkarsh, Alan Edelman et al. · arXiv.org · 0 citations

#machine learning Preprint Jul 2025

Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations

PEM-UDE, a method that combines prediction-error methodology with universal differential equations to discover governing equations from limited, noise-corrupted observations, yields a multi-scale neural mass model that ties single-neuron parameters to macroscopic network dynamics and predicts a relationship between con...

Anthony G. Chesebro, David Hofmann, V. Dixit et al. · 1 citation