Research on Julia is carried out at the Julia Lab at MIT and at many universities worldwide. If you use Julia in your research we request citing the following paper:
- Scalable Hamming Distance Computation Using Accelerated Matrix Transformations
Rabab M. Alomairy, Q. Cao, Hatem Ltaief, David E. Keyes, A. Edelman.
(2025). - C codegen considered unnecessary: go directly to binary , do not pass C . Compilation of Julia code for deployment in model-based engineering
Fredrik Bagge Carlson, Cody Tapscott, Gabriel Baraldi, Chris Rackauckas.
arXiv (2025). DOI:10.48550/arXiv.2502.01128. - A New Upper Bound For the Growth Factor in Gaussian Elimination with Complete Pivoting
Ankit Bisain, Alan Edelman, John Urschel.
arXiv (2025). DOI:10.48550/arXiv.2312.00994. - Scalable higher-order nonlinear solvers via higher-order automatic differentiation
Songchen Tan, Keming Miao, Alan Edelman, Christopher Rackauckas.
arXiv (2025). DOI:10.48550/arXiv.2501.16895. - On the Limit of the Tridiagonal Model for \$β\$- Dyson Brownian Motion
Alan Edelman, Sungwoo Jeong, Ron Nissim.
arXiv (2024). DOI:10.48550/arXiv.2411.01633. - Solving the Convex Flow Problem
Theo Diamandis, Guillermo Angeris.
arXiv (2024). DOI:10.48550/arXiv.2408.11040. - Some New Results on the Maximum Growth Factor in Gaussian Elimination
Alan Edelman, John Urschel.
SIAM Journal on Matrix Analysis and Applications (2024). DOI:10.1137/23M1571903. - On a perturbation analysis of Higham squared maximum Gaussian elimination growth matrices
Alan Edelman, John Urschel, Bowen Zhu.
arXiv (2024). DOI:10.48550/arXiv.2406.00737. - Mapping Out Phase Diagrams with Generative Classifiers
Julian Arnold, Frank Schäfer, Alan Edelman, Christoph Bruder.
Physical Review Letters (2024). DOI:10.1103/PhysRevLett.132.207301. - Convex Network Flows
Theo Diamandis, Guillermo Angeris, Alan Edelman.
arXiv (2024). DOI:10.48550/arXiv.2404.00765. - Backpropagation through Back Substitution with a Backslash
Alan Edelman, Ekin Akyürek, Yuyang Wang.
SIAM Journal on Matrix Analysis and Applications (2024). DOI:10.1137/22M1532871. - NonlinearSolve .jl: High - Performance and Robust Solvers for Systems of Nonlinear Equations in Julia
Avik Pal, Flemming Holtorf, Axel Larsson, Torkel Loman, Utkarsh, Frank Schäefer, Qingyu Qu, Alan Edelman, Chris Rackauckas.
arXiv (2024). DOI:10.48550/arXiv.2403.16341. - Automated translation and accelerated solving of differential equations on multiple GPU platforms
Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma, Tim Besard, Prakitr Srisuma, Tim Gymnich, Adam R. Gerlach, Alan Edelman, George Barbastathis, Richard D. Braatz, Christopher Rackauckas.
Computer Methods in Applied Mechanics and Engineering (2024). DOI:10.1016/j.cma.2023.116591. - Automating Heterogeneous Parallelism in Numerical Differential Equations
Utkarsh.
(2024). - Performance Bounds for Quantum Feedback Control
Flemming Holtorf, Frank Schäfer, Julian Arnold, Christopher Rackauckas, Alan Edelman.
IEEE Transactions on Automatic Control (2024). DOI:10.1109/TAC.2024.3416008. - Oceananigans.jl: A Julia library that achieves breakthrough resolution , memory and energy efficiency in global ocean simulations
Simone Silvestri, Gregory L. Wagner, Christopher Hill, Matin Raayai Ardakani, Johannes Blaschke, Jean-Michel Campin, Valentin Churavy, Navid C. Constantinou, Alan Edelman, John Marshall, Ali Ramadhan, Andre Souza, Raffaele Ferrari.
arXiv (2024). DOI:10.48550/arXiv.2309.06662. - A Fully Adaptive Radau Method for the Efficient Solution of Stiff Ordinary Differential Equations at Low Tolerances
Shreyas Ekanathan, Oscar Smith, Christopher Rackauckas.
arXiv (2024). DOI:10.48550/arXiv.2412.14362. - DataInterpolations .jl: Fast Interpolations of 1D data
Sathvik Bhagavan, Bart de Koning, Shubham Maddhashiya, Christopher Rackauckas.
Journal of Open Source Software (2024). DOI:10.21105/joss.06917. - Increasing spectral DCM flexibility and speed by leveraging Julia ’s ModelingToolkit and automated differentiation
David Hofmann, Anthony G. Chesebro, Chris Rackauckas, Lilianne R. Mujica-Parodi, Karl J. Friston, Alan Edelman, Helmut H. Strey.
bioRxiv (2024). DOI:10.1101/2023.10.27.564407. - Hybrid Symbolic - Numeric and Numerically - Assisted Symbolic Integration
Shahriar Iravanian, Shashi Gowda, Christopher Rackauckas.
Association for Computing Machinery (2024). DOI:10.1145/3666000.3669714. - Differentiable Programming for Differential Equations : A Review
Facundo Sapienza, Jordi Bolibar, Frank Schäfer, Brian Groenke, Avik Pal, Victor Boussange, Patrick Heimbach, Giles Hooker, Fernando Pérez, Per-Olof Persson, Christopher Rackauckas.
arXiv (2024). DOI:10.48550/arXiv.2406.09699. - SBMLToolkit .jl: a Julia package for importing SBML into the SciML ecosystem
Paul F. Lang, Anand Jain, Christopher Rackauckas.
Journal of Integrative Bioinformatics (2024). DOI:10.1515/jib-2024-0003. - Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning
Emily Nieves, Raj Dandekar, Chris Rackauckas.
Frontiers in Systems Biology (2024). DOI:10.3389/fsysb.2024.1338518. - Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach
Vinicius V. Santana, Erbet Costa, Carine M. Rebello, Ana Mafalda Ribeiro, Christopher Rackauckas, Idelfonso B. R. Nogueira.
Chemical Engineering Science (2023). DOI:10.1016/j.ces.2023.119223. - Forecasting virus outbreaks with social media data via neural ordinary differential equations
Matías Núñez, Nadia L. Barreiro, Rafael A. Barrio, Christopher Rackauckas.
Scientific Reports (2023). DOI:10.1038/s41598-023-37118-9. - Physics-enhanced deep surrogates for partial differential equations
Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson.
Nature Machine Intelligence (2023). DOI:10.1038/s42256-023-00761-y. - Advancing Odor Classification Models Enhanced by Scientific Machine Learning and Mechanistic Model : Probabilistic Weight Assignment for Odor Intensity Prediction and Uncertainty Analysis for Robust Fragrance Classification
Vinicius Santana, Erbet Costa, Carine Rebello, Ana Mafalda Ribeiro, Chris Rackauckas, Idelfonso Nogueira.
ChemRxiv (2023). DOI:10.26434/chemrxiv-2023-mtfrc. - Catalyst: Fast and flexible modeling of reaction networks
Torkel E. Loman, Yingbo Ma, Vasily Ilin, Shashi Gowda, Niklas Korsbo, Nikhil Yewale, Chris Rackauckas, Samuel A. Isaacson.
PLOS Computational Biology (2023). DOI:10.1371/journal.pcbi.1011530. - Differentiable modelling to unify machine learning and physical models for geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Yalan Song, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Chris Rackauckas, Binayak Mohanty, Tirthankar Roy, Chonggang Xu, Kathryn Lawson.
Nature Reviews Earth \& Environment (2023). DOI:10.1038/s43017-023-00450-9. - A differentiable , physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, Chaopeng Shen.
Biogeosciences (2023). DOI:10.5194/bg-20-2671-2023. - Differentiating Metropolis - Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer.
arXiv (2023). DOI:10.48550/arXiv.2306.07961. - Extending JumpProcess .jl for fast point process simulation with time-varying intensities
Guilherme Augusto Zagatti, Samuel A. Isaacson, Christopher Rackauckas, Vasily Ilin, See-Kiong Ng, Stéphane Bressan.
(2023). DOI:10.21105/jcon.00133. - Julia for biologists
Elisabeth Roesch, Joe G. Greener, Adam L. MacLean, Huda Nassar, Christopher Rackauckas, Timothy E. Holy, Michael P. H. Stumpf.
Nature Methods (2023). DOI:10.1038/s41592-023-01832-z. - A Practitioner 's Guide to Bayesian Inference in Pharmacometrics using Pumas
Mohamed Tarek, Jose Storopoli, Casey Davis, Chris Elrod, Julius Krumbiegel, Chris Rackauckas, Vijay Ivaturi.
arXiv (2023). DOI:10.48550/arXiv.2304.04752. - Robust Parameter Estimation for Rational Ordinary Differential Equations
Oren Bassik, Yosef Berman, Soo Go, Hoon Hong, Ilia Ilmer, Alexey Ovchinnikov, Chris Rackauckas, Pedro Soto, Chee Yap.
arXiv (2023). DOI:10.48550/arXiv.2303.02159. - Sum-of- Squares Bounds for Quantum Optimal Control
Flemming Holtorf, Frank Schäfer, Julian Arnold, Christopher Rackauckas, Alan Edelman.
(2023). DOI:10.1109/QCE57702.2023.10284. - Locally Regularized Neural Differential Equations : Some Black Boxes were meant to remain closed!
Avik Pal, Alan Edelman, Christopher Vincent Rackauckas.
PMLR (2023). - Fifty Three Matrix Factorizations : A Systematic Approach
Alan Edelman, Sungwoo Jeong.
SIAM Journal on Matrix Analysis and Applications (2023). DOI:10.1137/21M1416035. - The conditional DPP approach to random matrix distributions
Alan Edelman, Sungwoo Jeong.
arXiv (2023). DOI:10.48550/arXiv.2304.09319. - On the structure of the solutions to the matrix equation \textit G ⁎\textit JG = \textit J
Alan Edelman, Sungwoo Jeong.
Linear Algebra and its Applications (2023). DOI:10.1016/j.laa.2022.10.007. - Bridging HPC Communities through the Julia Programming Language
Valentin Churavy, William F. Godoy, Carsten Bauer, Hendrik Ranocha, Michael Schlottke-Lakemper, Ludovic Räss, Johannes Blaschke, Mosè Giordano, Erik Schnetter, Samuel Omlin, Jeffrey S. Vetter, Alan Edelman.
arXiv (2022). DOI:10.48550/arXiv.2211.02740. - AutoMat : Automated materials discovery for electrochemical systems
Emil Annevelink, Rachel Kurchin, Eric Muckley, Lance Kavalsky, Vinay I. Hegde, Valentin Sulzer, Shang Zhu, Jiankun Pu, David Farina, Matthew Johnson, Dhairya Gandhi, Adarsh Dave, Hongyi Lin, Alan Edelman, Bharath Ramsundar, James Saal, Christopher Rackauckas, Viral Shah, Bryce Meredig, Venkatasubramanian Viswanathan.
MRS Bulletin (2022). DOI:10.1557/s43577-022-00424-0. - On the Cartan decomposition for classical random matrix ensembles
Alan Edelman, Sungwoo Jeong.
Journal of Mathematical Physics (2022). DOI:10.1063/5.0087010. - Stably Accelerating Stiff Quantitative Systems Pharmacology Models : Continuous - Time Echo State Networks as Implicit Machine Learning
Ranjan Anantharaman, Anas Abdelrehim, Anand Jain, Avik Pal, Danny Sharp, { Utkarsh }, Alan Edelman, Chris Rackauckas.
IFAC-PapersOnLine (2022). DOI:10.1016/j.ifacol.2023.01.004. - Mixing Implicit and Explicit Deep Learning with Skip DEQs and Infinite Time Neural ODEs ( Continuous DEQs )
Avik Pal, Alan Edelman, Chris Rackauckas.
(2022). DOI:10.48550/arXiv.2201.12240. - Automatic Differentiation of Programs with Discrete Randomness
Gaurav Arya, Moritz Schauer, Frank Schäfer, Christopher Rackauckas.
Advances in Neural Information Processing Systems (2022). - Symbolic-numeric integration of univariate expressions based on sparse regression
Shahriar Iravanian, Carl Julius Martensen, Alessandro Cheli, Shashi Gowda, Anand Jain, Yingbo Ma, Chris Rackauckas.
ACM Commun. Comput. Algebra (2022). DOI:10.1145/3572867.3572882. - Continuous-time echo state networks for predicting power system dynamics
Ciaran Roberts, José Daniel Lara, Rodrigo Henriquez-Auba, Matthew Bossart, Ranjan Anantharaman, Chris Rackauckas, Bri-Mathias Hodge, Duncan S. Callaway.
Electric Power Systems Research (2022). DOI:10.1016/j.epsr.2022.108562. - Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
Varun Shankar, Gavin D. Portwood, Arvind T. Mohan, Peetak P. Mitra, Dilip Krishnamurthy, Christopher Rackauckas, Lucas A. Wilson, David P. Schmidt, Venkatasubramanian Viswanathan.
Physics of Fluids (2022). DOI:10.1063/5.0122115. - Differentiable State - Space Models and Hamiltonian Monte Carlo Estimation
David Childers, Jesús Fernández-Villaverde, Jesse Perla, Christopher Rackauckas, Peifan Wu.
National Bureau of Economic Research (2022). DOI:10.3386/w30573. - Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations
{ Utkarsh }, Chris Elrod, Yingbo Ma, Konstantin Althaus, Christopher Rackauckas.
(2022). DOI:10.1109/HPEC55821.2022.9926357. - Stochastic Optimal Control via Local Occupation Measures
Flemming Holtorf, Alan Edelman, Christopher Rackauckas.
arXiv (2025). DOI:10.48550/arXiv.2211.15652. - Model- Form Epistemic Uncertainty Quantification for Modeling with Differential Equations : Application to Epidemiology
Erin C. S. Acquesta, Teresa Portone, Raj Dandekar, Chris Rackauckas, Rileigh Bandy, Gabriel Huerta, India Dytzel.
(2022). DOI:10.2172/1888443. - DelayDiffEq : Generating Delay Differential Equation Solvers via Recursive Embedding of Ordinary Differential Equation Solvers
David Widmann, Chris Rackauckas.
arXiv (2022). DOI:10.48550/arXiv.2208.12879. - GlobalSensitivity .jl: Performant and Parallel Global Sensitivity Analysis with Julia
Vaibhav Kumar Dixit, Christopher Rackauckas.
Journal of Open Source Software (2022). DOI:10.21105/joss.04561. - Two heads are better than one: current landscape of integrating QSP and machine learning
Tongli Zhang, Ioannis P. Androulakis, Peter Bonate, Limei Cheng, Tomáš Helikar, Jaimit Parikh, Christopher Rackauckas, Kalyanasundaram Subramanian, Carolyn R. Cho, Ioannis P. Androulakis, Peter Bonate, Ivan Borisov, Gordon Broderick, Limei Cheng, Valeriu Damian, Rafael Dariolli, Oleg Demin, Nicholas Ellinwood, Dirk Fey, Abhishek Gulati, Tomas Helikar, Eric Jordie, Cynthia Musante, Jaimit Parikh, Christopher Rackauckas, Julio Saez-Rodriguez, Eric Sobie, Kalyanasundaram Subramanian, Carolyn R. Cho, { on behalf of the Working Group }.
Journal of Pharmacokinetics and Pharmacodynamics (2022). DOI:10.1007/s10928-022-09805-z. - ReservoirComputing .jl: An Efficient and Modular Library for Reservoir Computing Models
Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora.
Journal of Machine Learning Research (2022). - Plots.jl -- a user extendable plotting API for the julia programming language
Simon Christ, Daniel Schwabeneder, Christopher Rackauckas, Michael Krabbe Borregaard, Thomas Breloff.
arXiv (2022). DOI:10.48550/arXiv.2204.08775. - Differential methods for assessing sensitivity in biological models
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange.
PLOS Computational Biology (2022). DOI:10.1371/journal.pcbi.1009598. - Constrained Smoothers for State Estimation of Vapor Compression Cycles
Vedang M. Deshpande, Christopher R. Laughman, Yingbo Ma, Chris Rackauckas.
(2022). DOI:10.23919/ACC53348.2022.9867269. - AbstractDifferentiation .jl: Backend - Agnostic Differentiable Programming in Julia
Frank Schäfer, Mohamed Tarek, Lyndon White, Chris Rackauckas.
arXiv (2022). DOI:10.48550/arXiv.2109.12449. - Composing Modeling And Simulation With Machine Learning In Julia
Chris Rackauckas, Maja Gwozdz, Anand Jain, Yingbo Ma, Francesco Martinuzzi, Utkarsh Rajput, Elliot Saba, Viral B. Shah, Ranjan Anantharaman, Alan Edelman, Shashi Gowda, Avik Pal, Chris Laughman.
(2022). DOI:10.23919/ANNSIM55834.2022.9859453. - ModelingToolkit : A Composable Graph Transformation System For Equation - Based Modeling
Yingbo Ma, Shashi Gowda, Ranjan Anantharaman, Chris Laughman, Viral Shah, Chris Rackauckas.
arXiv (2022). DOI:10.48550/arXiv.2103.05244. - Data- Efficient Training with Physics - Enhanced Deep Surrogates
Raphaël Pestourie, Youssef Mroueh, Christopher Vincent Rackauckas, Payel Das, Steven Glenn Johnson.
(2021). - High-performance symbolic-numerics via multiple dispatch
Shashi Gowda, Yingbo Ma, Alessandro Cheli, Maja Gwóźzdź, Viral B. Shah, Alan Edelman, Christopher Rackauckas.
ACM Communications in Computer Algebra (2021). DOI:10.1145/3511528.3511535. - A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions
Yingbo Ma, Vaibhav Dixit, Michael J Innes, Xingjian Guo, Chris Rackauckas.
(2021). DOI:10.1109/HPEC49654.2021.9622796. - Stiff neural ordinary differential equations
Suyong Kim, Weiqi Ji, Sili Deng, Yingbo Ma, Christopher Rackauckas.
Chaos: An Interdisciplinary Journal of Nonlinear Science (2021). DOI:10.1063/5.0060697. - NeuralPDE : Automating Physics - Informed Neural Networks ( PINNs ) with Error Approximations
Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, Nand Vinchhi, Kaushik Balakrishnan, Devesh Upadhyay, Chris Rackauckas.
arXiv (2021). DOI:10.48550/arXiv.2107.09443. - Opening the Blackbox : Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Avik Pal, Yingbo Ma, Viral Shah, Christopher V. Rackauckas.
PMLR (2021). - Collocation based training of neural ordinary differential equations
Elisabeth Roesch, Christopher Rackauckas, Michael P. H. Stumpf.
Statistical Applications in Genetics and Molecular Biology (2021). DOI:10.1515/sagmb-2020-0025. - Safe Blues : The case for virtual safe virus spread in the long-term fight against epidemics
Raj Dandekar, Shane G. Henderson, Hermanus M. Jansen, Joshua McDonald, Sarat Moka, Yoni Nazarathy, Christopher Rackauckas, Peter G. Taylor, Aapeli Vuorinen.
Patterns (2021). DOI:10.1016/j.patter.2021.100220. - Hybrid Mechanistic + Neural Model of Laboratory Helicopter
Christopher Rackauckas, Roshan Sharma, Bernt Lie.
(2021). - Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations of Bayesian Posteriors with Pumas
Chris Rackauckas, Vaibhav Dixit, Adam R. Gerlach, Vijay Ivaturi.
bioRxiv (2021). DOI:10.1101/2021.01.25.428134. - Implications of Delayed Reopening in Controlling the COVID -19 Surge in Southern and West - Central USA
Raj Dandekar, Emma Wang, George Barbastathis, Chris Rackauckas.
Health Data Science (2021). DOI:10.34133/2021/9798302. - Neural Network Surrogates and Symbolic Regression for System Estimation
Carl Julius Martensen, Christopher Rackauckas.
(2021). - The densities and distributions of the largest eigenvalue and the trace of a Beta – Wishart matrix
Vesselin Drensky, Alan Edelman, Tierney Genoar, Raymond Kan, Plamen Koev.
Random Matrices: Theory and Applications (2021). DOI:10.1142/S2010326321500106. - Generalized physics-informed learning through language-wide differentiable programming
C. Rackauckas, A. Edelman, K. Fischer, M. Innes, E. Saba, V. B. Shah, W. Tebbutt.
MIT web domain (2021). - A simple model for assessing climate control trade-offs and responding to unanticipated climate outcomes
Henri F Drake, Ronald L Rivest, Alan Edelman, John Deutch.
Environmental Research Letters (2021). DOI:10.1088/1748-9326/ac243e. - Circuitscape in Julia : Empowering Dynamic Approaches to Connectivity Assessment
Kimberly R. Hall, Ranjan Anantharaman, Vincent A. Landau, Melissa Clark, Brett G. Dickson, Aaron Jones, Jim Platt, Alan Edelman, Viral B. Shah.
Land (2021). DOI:10.3390/land10030301. - Composable and Reusable Neural Surrogates to Predict System Response of Causal Model Components
Ranjan Anantharaman, Anas Abdelrehim, Francesco Martinuzzi, Sharan Yalburgi, Elliot Saba, Keno Fischer, Glen Hertz, Pepijn de Vos, Chris Laughman, Yingbo Ma, Viral Shah, Alan Edelman, Chris Rackauckas.
(2021). - Accelerating Simulation of Stiff Nonlinear Systems using Continuous - Time Echo State Networks
Ranjan Anantharaman, Yingbo Ma, Shashi Gowda, Chris Laughman, Viral Shah, Alan Edelman, Chris Rackauckas.
arXiv (2021). DOI:10.48550/arXiv.2010.04004. - The gsvd: where are the ellipses? , matrix trigonometry , and more
A. Edelman, Y. WANG.
SIAM Journal on Matrix Analysis and Applications (2021). DOI:10.1137/18M1234412. - Bayesian Neural Ordinary Differential Equations
R. Dandekar, K. Chung, V. Dixit, M. Tarek, A. Garcia-Valadez, K. Vishal Vemula, C. Rackauckas.
LAFI (2021). - AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
F. Schäfer, M Tarek, L. White, C. Rackauckas.
arXiv e-prints (2021). - Signal Enhancement for Magnetic Navigation Challenge Problem
A.R. Gnadt, J. Belarge, A. Canciani, G. Carl, L. Conger, J. Curro, A. Edelman, P. Morales, A.P. Nielsen, M.F. O'Keeffe, C.V. Rackauckas, J. Taylor, A.B. Wollaber.
arXiv (2020). DOI:10.48550/arXiv.2007.12158. - Machine Learning-Enhanced Magnetic Calibration for Airborne Magnetic Anomaly Navigation
A.R. Gnadt.
AIAA SCITECH 2022 Forum (2022). DOI:10.2514/6.2022-1760. - Derivation and Extensions of the Tolles-Lawson Model for Aeromagnetic Compensation
A.R. Gnadt, A.B. Wollaber, A.P. Nielsen.
arXiv (2022). DOI:10.48550/arXiv.2212.09899. - Circuitscape in julia: Empowering dynamic approaches to connectivity assessment
K.R. Hall, R. Anantharaman, V.A. Landau, M. Clark, B.G. Dickson, A. Jones, J. Platt, A. Edelman, V.B. Shah.
Land (2021). DOI:10.3390/land10030301. - Accelerating simulation of stiff nonlinear systems using continuous-time echo state networks
R. Anantharaman, Y. Ma, S. Gowda, C. Laughman, V.B. Shah, A. Edelman, C. Rackauckas.
CEUR Workshop Proceedings (2021). - Uncertainty Quantification of Ocean Parameterizations : Application to the K - Profile - Parameterization for Penetrative Convection
A. N. Souza, G. L. Wagner, A. Ramadhan, B. Allen, V. Churavy, J. Schloss, J. Campin, C. Hill, A. Edelman, J. Marshall, G. Flierl, R. Ferrari.
Journal of Advances in Modeling Earth Systems (2020). DOI:10.1029/2020MS002108. - Oceananigans.jl: Fast and friendly geophysical fluid dynamics on GPUs
Ali Ramadhan, Gregory LeClaire Wagner, Chris Hill, Jean-Michel Campin, Valentin Churavy, Tim Besard, Andre Souza, Alan Edelman, Raffaele Ferrari, John Marshall.
Journal of Open Source Software (2020). DOI:10.21105/joss.02018. - Uncertainty Quantification of Ocean Parameterizations: Application to the K-Profile-Parameterization for Penetrative Convection
A.N. Souza, G.L. Wagner, A. Ramadhan, B. Allen, V. Churavy, J. Schloss, J. Campin, C. Hill, A. Edelman, J. Marshall, G. Flierl, R. Ferrari.
Journal of Advances in Modeling Earth Systems (2020). DOI:10.1029/2020MS002108. - A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread
R. Dandekar, C. Rackauckas, G. Barbastathis.
Patterns (2020). DOI:10.1016/j.patter.2020.100145. - Beyond Deterministic Models in Drug Discovery and Development
I. Irurzun-Arana, C. Rackauckas, T.O. McDonald, I.F. Trocóniz.
Trends in Pharmacological Sciences (2020). DOI:10.1016/j.tips.2020.09.005. - Generalized physics-informed learning through language-wide differentiable programming
C. Rackauckas, A. Edelman, K. Fischer, M. Innes, E. Saba, V.B. Shah, W. Tebbutt.
CEUR Workshop Proceedings (2020). - ACED: Accelerated Computational Electrochemical systems Discovery
R.C. Kurchin, E. Muckley, L. Kavalsky, V. Hegde, D. Gandhi, X. Sun, M. Johnson, A. Edelman, J. Saal, C.V. Rackauckas, B. Meredig, V. Shah, V. Viswanathan.
NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning (2020). - Stability-Optimized High Order Methods and Stiffness Detection for Pathwise Stiff Stochastic Differential Equations
C. Rackauckas, Q. Nie.
2020 IEEE High Performance Extreme Computing Conference (HPEC) (2020). DOI:10.1109/HPEC43674.2020.9286178. - Instead of rewriting foreign code for machine learning , automatically synthesize fast gradients
W.S. Moses, V. Churavy.
Advances in Neural Information Processing Systems (2020). - StochasticDelayDiffEq.jl - An Integrator Interface for Stochastic Delay Differential Equations in Julia
H.T. Sykora, C.V. Rackauckas, D. Widmann, D. Bachrathy.
ENOC 2020 (2020). - Learning non-linear spatio-temporal dynamics with convolutional Neural ODEs
V. Shankar, G. Portwood, A. Mohan, P. Mitra, C. Rackauckas, L. Wilson, D. Schmidt, V. Viswanathan.
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS) (2020). - On computing Schur functions and series thereof
C. Chan, V. Drensky, A. Edelman, R. Kan, P. Koev.
Journal of Algebraic Combinatorics (2019). DOI:10.1007/s10801-018-0846-y. - Rapid software prototyping for heterogeneous and distributed platforms
T. Besard, V. Churavy, A. Edelman, B.D. Sutter.
Advances in Engineering Software (2019). DOI:10.1016/j.advengsoft.2019.02.002. - Confederated modular differential equation APIs for accelerated algorithm development and benchmarking
C. Rackauckas, Q. Nie.
Advances in Engineering Software (2019). DOI:10.1016/j.advengsoft.2019.03.009. - Circuit-theory applications to connectivity science and conservation
B.G. Dickson, C.M. Albano, R. Anantharaman, P. Beier, J. Fargione, T.A. Graves, M.E. Gray, K.R. Hall, J.J. Lawler, P.B. Leonard, C.E. Littlefield, M.L. McClure, J. Novembre, C.A. Schloss, N.H. Schumaker, V.B. Shah, D.M. Theobald.
Conservation Biology (2019). DOI:10.1111/cobi.13230. - Highly-Ccalable , physics-informed GANs for learning solutions of stochastic PDEs
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