Published in arXiv preprint arXiv:1902.02376, 2019
This paper introduces DiffEqFlux.jl, a Julia library for neural differential equations.
Recommended citation: Rackauckas, C., Innes, M., Ma, Y., Bettencourt, J., White, L., & Dixit, V. (2019). "DiffEqFlux.jl - A Julia Library for Neural Differential Equations." arXiv preprint arXiv:1902.02376. https://arxiv.org/abs/1902.02376
Published in BioRxiv, 2020
This paper presents Pumas, a high-performance platform for predictive healthcare analytics.
Recommended citation: Rackauckas, C., Ma, Y., Noack, A., Dixit, V., Mogensen, P. K., Byrne, S., ... & others. (2020). "Accelerated Predictive Healthcare Analytics with Pumas, a High-Performance Pharmaceutical Modeling and Simulation Platform." BioRxiv. https://www.biorxiv.org/content/10.1101/2020.11.28.402297v1
Published in POPL-LAFI, 2021
This paper introduces Bayesian neural ordinary differential equations.
Recommended citation: Dandekar, R., Chung, K., Dixit, V., Tarek, M., Garcia-Valadez, A., Vemula, K. V., & Rackauckas, C. (2021). "Bayesian Neural Ordinary Differential Equations." POPL-LAFI. https://popl21.sigplan.org/details/lafi-2021-papers/7/Bayesian-Neural-Ordinary-Differential-Equations
Published in BioRxiv, 2021
This paper presents efficient precision dosing under estimated uncertainties using Koopman expectations of Bayesian posteriors with Pumas.
Recommended citation: Rackauckas, C., Dixit, V., Gerlach, A. R., & Ivaturi, V. (2021). "Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations of Bayesian Posteriors with Pumas." BioRxiv. https://www.biorxiv.org/content/10.1101/2021.01.25.428134v1
Published in 2021 IEEE High Performance Extreme Computing Conference (HPEC), 2021
This paper compares automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions.
Recommended citation: Ma, Y., Dixit, V., Innes, M. J., Guo, X., & Rackauckas, C. (2021). "A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions." 2021 IEEE High Performance Extreme Computing Conference (HPEC). https://ieeexplore.ieee.org/document/9622796
Published in Journal of Open Source Software, 2022
This paper presents GlobalSensitivity.jl, a Julia package for performant and parallel global sensitivity analysis.
Recommended citation: Dixit, V. K., & Rackauckas, C. (2022). "GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia." Journal of Open Source Software, 7(76), 4561. https://joss.theoj.org/papers/10.21105/joss.04561