Publications: Automating High-Performance Computing and Physics-Informed Learning

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:

Julia: A Fresh Approach to Numerical Computing

Jeff Bezanson Alan Edelman Stefan Karpinski Viral B. Shah.

SIAM Review 59: 65–98 (2017). DOI: 10.1137/141000671.

Selected publications from the last five years

• A simple model for assessing climate control trade-offs and responding to unanticipated climate outcomes
  H.F. Drake, R.L. Rivest, A. Edelman, J. Deutch.
  Environmental Research Letters (2021). DOI:10.1088/1748-9326/ac243e.
  
• 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.
  
• 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.
  
• The densities and distributions of the largest eigenvalue and the trace of a Beta-Wishart matrix
  V. Drensky, A. Edelman, T. Genoar, R. Kan, P. Koev.
  Random Matrices: Theory and Application (2021). DOI:10.1142/S2010326321500106.
  
• Stiff neural ordinary differential equations
  S. Kim, W. Ji, S. Deng, Y. Ma, C. Rackauckas.
  Chaos (2021). DOI:10.1063/5.0060697.
  
• Collocation based training of neural ordinary differential equations
  E. Roesch, C. Rackauckas, M.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
  R. Dandekar, S.G. Henderson, H.M. Jansen, J. McDonald, S. Moka, Y. Nazarathy, C. Rackauckas, P.G. Taylor, A. Vuorinen.
  Patterns (2021). DOI:10.1016/j.patter.2021.100220.
  
• 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).
  
• Bayesian Neural Ordinary Differential Equations
  R. Dandekar, K. Chung, V. Dixit, M. Tarek, A. Garcia-Valadez, K. Vishal Vemula, C. Rackauckas.
  LAFI (2021).
  
• Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
  A. Pal, Y. Ma, V. Shah, C.V. Rackauckas.
  Proceedings of the 38th International Conference on Machine Learning (2021).
  
• Hybrid Mechanistic + Neural Model of Laboratory Helicopter
  C. Rackauckas, R. Sharma, B. Lie.
  Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020 (2021). DOI:10.3384/ecp20176264.
  
• High-performance symbolic-numerics via multiple dispatch
  S. Gowda, Y. Ma, A. Cheli, M. Gwozdz, V.B. Shah, A. Edelman, C. Rackauckas.
  arXiv (2021).
  
• Composing Modeling and Simulation with Machine Learning in Julia
  C. Rackauckas, R Anantharaman, A. Edelman, S. Gowda, M. Gwozdz, A. Jain, C. Laughman, Y. Ma, F. Martinuzzi, A. Pal, U. Rajput, E. Saba, V.B. Shah.
  arXiv (2021).
  
• AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
  F. Schäfer, M Tarek, L. White, C. Rackauckas.
  arXiv e-prints (2021).
  
• Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA
  R. Dandekar, E. Wang, G. Barbastathis, C. Rackauckas.
  Health Data Science (2021). DOI:https://doi-org.ezproxy.canberra.edu.au/10.34133/2021/9798302.
  
• 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.
  
• 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
  L. Yang, S. Treichler, T. Kurth, K. Fischer, D. Barajas-Solano, J. Romero, V. Churavy, A. Tartakovsky, M. Houston, Prabhat, G. Karniadakis.
  Proceedings of DLS 2019: Deep Learning on Supercomputers - Held in conjunction with SC 2019: The International Conference for High Performance Computing , Networking , Storage and Analysis (2019). DOI:10.1109/DLS49591.2019.00006.
  
• 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.
  
• Sparsity Programming: Automated Sparsity-Aware Optimizations in Differentiable Programming
  S. Gowda, Y. Ma, V. Churavy, A. Edelman, C. Rackauckas.
  NeurIPS 2019 Program Transformations for Machine Learning Workshop (2019).
  
• Interdisciplinary Case Study: How Mathematicians and Biologists Found Order in Cellular Noise
  C. Rackauckas, T. Schilling, Q. Nie.
  iScience (2018). DOI:10.1016/j.isci.2018.10.002.
  
• Mean-Independent Noise Control of Cell Fates via Intermediate States
  C. Rackauckas, T. Schilling, Q. Nie.
  iScience (2018). DOI:10.1016/j.isci.2018.04.002.
  
• Julia: A fresh approach to numerical computing
  J. Bezanson, A. Edelman, S. Karpinski, V.B. Shah.
  SIAM Review (2017). DOI:10.1137/141000671.
  
• gflow: software for modelling circuit theory-based connectivity at any scale
  P.B. Leonard, E.B. Duffy, R.F. Baldwin, B.H. McRae, V.B. Shah, T.K. Mohapatra.
  Methods in Ecology and Evolution (2017). DOI:10.1111/2041-210X.12689.