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2020 – today
- 2024
[j65]Roland Pulch, Adrian Sandu:
Rosenbrock-W methods for stochastic Galerkin systems. J. Comput. Appl. Math. 438: 115527 (2024)- [i73]Abhinab Bhattacharjee, Andrey A. Popov, Arash Sarshar, Adrian Sandu:
Improving the Adaptive Moment Estimation (ADAM) stochastic optimizer through an Implicit-Explicit (IMEX) time-stepping approach. CoRR abs/2403.13704 (2024) - [i72]Amit N. Subrahmanya, Andrey A. Popov, Reid Gomillion, Adrian Sandu:
Preserving Nonlinear Constraints in Variational Flow Filtering Data Assimilation. CoRR abs/2405.04380 (2024) - 2023
- [i71]Ali Haisam Muhammad Rafid, Adrian Sandu:
Neural Network Reduction with Guided Regularizers. CoRR abs/2305.18448 (2023) - [i70]Kevin Schäfers, Michael Günther, Adrian Sandu:
Symplectic multirate generalized additive Runge-Kutta methods for Hamiltonian systems. CoRR abs/2306.04389 (2023) - [i69]Ali Haisam Muhammad Rafid, Adrian Sandu:
Adversarial Training Using Feedback Loops. CoRR abs/2308.11881 (2023) - [i68]Adwait Verulkar, Corina Sandu, Adrian Sandu, Daniel Dopico:
Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities. CoRR abs/2312.15771 (2023) - 2022
- [j64]Andrey A. Popov, Adrian Sandu:
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders. Frontiers Appl. Math. Stat. 8: 904687 (2022) - [j63]Severiano González-Pinto
, Domingo Hernández-Abreu, Maria Soledad Pérez-Rodríguez, Arash Sarshar, Steven Roberts, Adrian Sandu:
A unified formulation of splitting-based implicit time integration schemes. J. Comput. Phys. 448: 110766 (2022) - [j62]Steven Roberts, Andrey A. Popov, Arash Sarshar, Adrian Sandu:
A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model. SIAM J. Sci. Comput. 44(3): 1405- (2022) - [j61]Steven R. Glandon, Mahesh Narayanamurthi, Adrian Sandu:
Linearly Implicit Multistep Methods for Time Integration. SIAM J. Sci. Comput. 44(6): 3437- (2022) - [i67]Steven Roberts, Adrian Sandu:
Eliminating Order Reduction on Linear, Time-Dependent ODEs with GARK Methods. CoRR abs/2201.07940 (2022) - [i66]Jostein Barry-Straume, Arash Sarshar, Andrey A. Popov, Adrian Sandu:
Physics-informed neural networks for PDE-constrained optimization and control. CoRR abs/2205.03377 (2022) - [i65]Andrey A. Popov, Arash Sarshar, Austin Chennault, Adrian Sandu:
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction. CoRR abs/2207.06676 (2022) - [i64]Andrey A. Popov, Adrian Sandu:
The Model Forest Ensemble Kalman Filter. CoRR abs/2210.11971 (2022) - [i63]Dylan Park, Changhong Mou, Honghu Liu, Adrian Sandu, Traian Iliescu:
A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations. CoRR abs/2211.12968 (2022) - 2021
- [j60]Paul Tranquilli, Ross Glandon, Adrian Sandu:
Subspace adaptivity in Rosenbrock-Krylov methods for the time integration of initial value problems. J. Comput. Appl. Math. 385: 113188 (2021) - [j59]Christoph Hachtel, Andreas Bartel, Michael Günther, Adrian Sandu:
Multirate implicit Euler schemes for a class of differential-algebraic equations of index-1. J. Comput. Appl. Math. 387: 112499 (2021) - [j58]Arash Sarshar, Steven Roberts, Adrian Sandu:
Alternating directions implicit integration in a general linear method framework. J. Comput. Appl. Math. 387: 112619 (2021) - [j57]Azam S. Zavar Moosavi, Vishwas Rao, Adrian Sandu:
Machine learning based algorithms for uncertainty quantification in numerical weather prediction models. J. Comput. Sci. 50: 101295 (2021) - [j56]Steven Roberts, John Loffeld, Arash Sarshar, Carol S. Woodward, Adrian Sandu:
Implicit Multirate GARK Methods. J. Sci. Comput. 87(1): 4 (2021) - [j55]Adrian Sandu, Vladimir Z. Tomov, Lenka Cervená, Tzanio V. Kolev:
Conservative High-Order Time Integration for Lagrangian Hydrodynamics. SIAM J. Sci. Comput. 43(1): A221-A241 (2021) - [j54]Andrey A. Popov, Changhong Mou, Adrian Sandu, Traian Iliescu:
A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates. SIAM J. Sci. Comput. 43(2): A1134-A1162 (2021) - [i62]Michael Günther, Adrian Sandu:
Multirate Linearly-Implicit GARK Schemes. CoRR abs/2102.10203 (2021) - [i61]Andrey A. Popov, Adrian Sandu:
Multifidelity Ensemble Kalman Filtering using surrogate models defined by Physics-Informed Autoencoders. CoRR abs/2102.13025 (2021) - [i60]Severiano González-Pinto, Domingo Hernández-Abreu, Maria Soledad Pérez-Rodríguez, Arash Sarshar, Steven Roberts, Adrian Sandu:
A unified formulation of splitting-based implicit time integration schemes. CoRR abs/2103.00757 (2021) - [i59]Michael Günther, Adrian Sandu, Antonella Zanna:
Symplectic GARK methods for Hamiltonian systems. CoRR abs/2103.04110 (2021) - [i58]Rachel Cooper, Andrey A. Popov, Adrian Sandu:
Investigation of Nonlinear Model Order Reduction of the Quasigeostrophic Equations through a Physics-Informed Convolutional Autoencoder. CoRR abs/2108.12344 (2021) - [i57]Andrey A. Popov, Amit N. Subrahmanya, Adrian Sandu:
A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter. CoRR abs/2109.09673 (2021) - [i56]Austin Chennault, Andrey A. Popov, Amit N. Subrahmanya, Rachel Cooper, Anuj Karpatne, Adrian Sandu:
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation. CoRR abs/2111.08626 (2021) - [i55]Amit N. Subrahmanya, Andrey A. Popov, Adrian Sandu:
An Ensemble Variational Fokker-Planck Method for Data Assimilation. CoRR abs/2111.13926 (2021) - 2020
- [j53]Steven Roberts, Arash Sarshar, Adrian Sandu:
Coupled Multirate Infinitesimal GARK Schemes for Stiff Systems with Multiple Time Scales. SIAM J. Sci. Comput. 42(3): A1609-A1638 (2020) - [i54]Mahesh Narayanamurthi, Ulrich Römer, Adrian Sandu:
Goal-oriented a posteriori estimation of numerical errors in the solution of multiphysics systems. CoRR abs/2001.08824 (2020) - [i53]Steven Roberts, Arash Sarshar, Adrian Sandu:
Parallel implicit-explicit general linear methods. CoRR abs/2002.00868 (2020) - [i52]Andrey A. Popov, Adrian Sandu, Elias David Niño Ruiz, Geir Evensen:
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering. CoRR abs/2003.00354 (2020) - [i51]Andrey A. Popov, Adrian Sandu:
An Explicit Probabilistic Derivation of Inflation in a Scalar Ensemble Kalman Filter for Finite Step, Finite Ensemble Convergence. CoRR abs/2003.13162 (2020) - [i50]Adrian Sandu:
Convergence Results for Implicit-Explicit General Linear Methods. CoRR abs/2004.04274 (2020) - [i49]Andrey A. Popov, Changhong Mou, Traian Iliescu, Adrian Sandu:
A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates. CoRR abs/2007.00793 (2020) - [i48]Adrian Sandu, Michael Günther, Steven Roberts:
Linearly implicit GARK schemes. CoRR abs/2008.01612 (2020) - [i47]Steven Roberts, Andrey A. Popov, Arash Sarshar, Adrian Sandu:
A fast time-stepping strategy for ODE systems equipped with a surrogate model. CoRR abs/2011.03688 (2020) - [i46]Ross Glandon, Mahesh Narayanamurthi, Adrian Sandu:
Linearly Implicit Multistep Methods for Time Integration. CoRR abs/2011.10685 (2020)
2010 – 2019
- 2019
- [j52]Elias David Niño Ruiz, Adrian Sandu:
Efficient parallel implementation of DDDAS inference using an ensemble Kalman filter with shrinkage covariance matrix estimation. Clust. Comput. 22(Suppl 1): 2211-2221 (2019) - [j51]John C. Little, Erich T. Hester, Sondoss ElSawah, George M. Filz, Adrian Sandu, Cayelan C. Carey, Takuya Iwanaga, Anthony J. Jakeman:
A tiered, system-of-systems modeling framework for resolving complex socio-environmental policy issues. Environ. Model. Softw. 112: 82-94 (2019) - [j50]Elias David Niño Ruiz, Adrian Sandu, Xinwei Deng:
A parallel implementation of the ensemble Kalman filter based on modified Cholesky decomposition. J. Comput. Sci. 36 (2019) - [j49]Mahesh Narayanamurthi, Paul Tranquilli, Adrian Sandu, Mayya Tokman:
EPIRK-W and EPIRK-K Time Discretization Methods. J. Sci. Comput. 78(1): 167-201 (2019) - [j48]Adrian Sandu:
A Class of Multirate Infinitesimal GARK Methods. SIAM J. Numer. Anal. 57(5): 2300-2327 (2019) - [j47]Arash Sarshar, Steven Roberts, Adrian Sandu:
Design of High-Order Decoupled Multirate GARK Schemes. SIAM J. Sci. Comput. 41(2): A816-A847 (2019) - [c53]Azam S. Zavar Moosavi, Vishwas Rao, Adrian Sandu:
A Learning-Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models. ICCS (4) 2019: 126-140 - [c52]Azam S. Zavar Moosavi, Ahmed Attia, Adrian Sandu:
Tuning Covariance Localization Using Machine Learning. ICCS (4) 2019: 199-212 - [i45]Steven Roberts, Andrey A. Popov, Adrian Sandu:
ODE Test Problems: a MATLAB suite of initial value problems. CoRR abs/1901.04098 (2019) - [i44]Arash Sarshar, Adrian Sandu:
Alternating Directions Implicit Integration in a General Linear Method Framework. CoRR abs/1902.00622 (2019) - [i43]Mahesh Narayanamurthi, Adrian Sandu:
Partitioned Exponential Methods for Coupled Multiphysics Systems. CoRR abs/1908.09434 (2019) - [i42]Ross Glandon, Paul Tranquilli, Adrian Sandu:
Biorthogonal Rosenbrock-Krylov time discretization methods. CoRR abs/1908.10531 (2019) - [i41]Paul Tranquilli, Ross Glandon, Adrian Sandu:
Adaptive Krylov-Type Time Integration Methods. CoRR abs/1910.02514 (2019) - [i40]Steven Roberts, John Loffeld, Arash Sarshar, Carol S. Woodward, Adrian Sandu:
Implicit multirate GARK methods. CoRR abs/1910.14079 (2019) - [i39]Mahesh Narayanamurthi, Adrian Sandu:
Efficient implementation of partitioned stiff exponential Runge-Kutta methods. CoRR abs/1912.01044 (2019) - 2018
- [j46]Razvan Stefanescu, Azam S. Zavar Moosavi, Adrian Sandu:
Parametric domain decomposition for accurate reduced order models: Applications of MP-LROM methodology. J. Comput. Appl. Math. 340: 629-644 (2018) - [j45]Ulrich Römer, Mahesh Narayanamurthi, Adrian Sandu:
Solving parameter estimation problems with discrete adjoint exponential integrators. Optim. Methods Softw. 33(4-6): 750-770 (2018) - [j44]Elias David Niño Ruiz, Adrian Sandu, Xinwei Deng:
An Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation. SIAM J. Sci. Comput. 40(2) (2018) - [i38]Azam S. Zavar Moosavi, Ahmed Attia, Adrian Sandu:
A Machine Learning Approach to Adaptive Covariance Localization. CoRR abs/1801.00548 (2018) - [i37]Azam S. Zavar Moosavi, Vishwas Rao, Adrian Sandu:
A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models. CoRR abs/1802.08055 (2018) - [i36]Arash Sarshar, Steven Roberts, Adrian Sandu:
Design of High-Order Decoupled Multirate GARK Schemes. CoRR abs/1804.07716 (2018) - [i35]Adrian Sandu:
A Class of Multirate Infinitesimal GARK Methods. CoRR abs/1808.02759 (2018) - [i34]Andrey A. Popov, Adrian Sandu:
A Bayesian Approach to Multivariate Adaptive Localization in Ensemble-Based Data Assimilation with Time-Dependent Extensions. CoRR abs/1809.08984 (2018) - [i33]Steven Roberts, Arash Sarshar, Adrian Sandu:
Coupled Multirate Infinitesimal GARK Schemes for Stiff Systems with Multiple Time Scales. CoRR abs/1812.00808 (2018) - 2017
- [j43]Paul Tranquilli, Ross Glandon, Arash Sarshar, Adrian Sandu:
Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations. J. Comput. Appl. Math. 310: 213-223 (2017) - [j42]Vishwas Rao, Adrian Sandu, Michael Ng, Elias David Niño Ruiz:
Robust Data Assimilation Using L1 and Huber Norms. SIAM J. Sci. Comput. 39(3) (2017) - [i32]Azam S. Zavar Moosavi, Razvan Stefanescu, Adrian Sandu:
Multivariate predictions of local reduced-order-model errors and dimensions. CoRR abs/1701.03720 (2017) - [i31]Mahesh Narayanamurthi, Paul Tranquilli, Adrian Sandu, Mayya Tokman:
EPIRK-W and EPIRK-K time discretization methods. CoRR abs/1701.06528 (2017) - [i30]Ulrich Römer, Mahesh Narayanamurthi, Adrian Sandu:
Solving Parameter Estimation Problems with Discrete Adjoint Exponential Integrators. CoRR abs/1704.02549 (2017) - [i29]Ahmed Attia, Adrian Sandu:
DATeS: A Highly-Extensible Data Assimilation Testing Suite. CoRR abs/1704.05594 (2017) - 2016
- [j41]Adrian Sandu:
Rosenbrock methods with an explicit first stage. Int. J. Comput. Math. 93(6): 995-1010 (2016) - [j40]Elias David Niño Ruiz, Adrian Sandu:
A derivative-free trust region framework for variational data assimilation. J. Comput. Appl. Math. 293: 164-179 (2016) - [j39]Vishwas Rao, Adrian Sandu:
A time-parallel approach to strong-constraint four-dimensional variational data assimilation. J. Comput. Phys. 313: 583-593 (2016) - [j38]Michael Günther, Adrian Sandu:
Multirate generalized additive Runge Kutta methods. Numerische Mathematik 133(3): 497-524 (2016) - [j37]Hong Zhang, Adrian Sandu, Sébastien Blaise:
High Order Implicit-explicit General Linear Methods with Optimized Stability Regions. SIAM J. Sci. Comput. 38(3) (2016) - [i28]Ahmed Attia, Razvan Stefanescu, Adrian Sandu:
The Reduced-Order Hybrid Monte Carlo Sampling Smoother. CoRR abs/1601.00129 (2016) - [i27]Elias D. Niño, Adrian Sandu, Xinwei Deng:
A Parallel Implementation of the Ensemble Kalman Filter Based on Modified Cholesky Decomposition. CoRR abs/1606.00807 (2016) - [i26]Ahmed Attia, Azam S. Zavar Moosavi, Adrian Sandu:
Cluster Sampling Filters for Non-Gaussian Data Assimilation. CoRR abs/1607.03592 (2016) - [i25]Arash Sarshar, Paul Tranquilli, Brent Pickering, Andrew McCall, Adrian Sandu, Christopher J. Roy:
A Numerical Investigation of Matrix-Free Implicit Time-Stepping Methods for Large CFD Simulations. CoRR abs/1607.06834 (2016) - 2015
- [j36]Tae-Hyuk Ahn, Adrian Sandu, Layne T. Watson, Clifford A. Shaffer, Yang Cao, William T. Baumann:
A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations. Int. J. Parallel Program. 43(4): 597-630 (2015) - [j35]Hong Zhang, Adrian Sandu, Paul Tranquilli:
Application of approximate matrix factorization to high order linearly implicit Runge-Kutta methods. J. Comput. Appl. Math. 286: 196-210 (2015) - [j34]Razvan Stefanescu, Adrian Sandu, Ionel Michael Navon:
POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation. J. Comput. Phys. 295: 569-595 (2015) - [j33]Vishwas Rao, Adrian Sandu:
A Posteriori Error Estimates for the Solution of Variational Inverse Problems. SIAM/ASA J. Uncertain. Quantification 3(1): 737-761 (2015) - [j32]Elias David Niño Ruiz, Adrian Sandu, Jeffrey L. Anderson:
An efficient implementation of the ensemble Kalman filter based on an iterative Sherman-Morrison formula. Stat. Comput. 25(3): 561-577 (2015) - [j31]Adrian Sandu, Michael Günther:
A Generalized-Structure Approach to Additive Runge-Kutta Methods. SIAM J. Numer. Anal. 53(1): 17-42 (2015) - [j30]Evgeniy Zharovsky, Adrian Sandu, Hong Zhang:
A Class Of Implicit-Explicit Two-Step Runge-Kutta Methods. SIAM J. Numer. Anal. 53(1): 321-341 (2015) - [c51]Aniruddha S. Gokhale, Salim Hariri, Adrian Sandu, Vaidy S. Sunderam:
Introduction to HiPC Workshops 3 and 4. HiPC Workshops 2015: 52 - [c50]Vishwas Rao, Adrian Sandu:
Parallel Solution of DDDAS Variational Inference Problems. ICCS 2015: 2474-2482 - [c49]Elias David Niño Ruiz, Adrian Sandu, Xinwei Deng:
A parallel ensemble Kalman filter implementation based on modified Cholesky decomposition. ScalA@SC 2015: 4:1-4:8 - [p3]Corina Sandu, Lin Li, Adrian Sandu:
Treatment of Uncertainties in Multibody Dynamic Systems using a Generalized Polynomial Chaos Approach; Case Study on a Full Vehicle. Advanced Autonomous Vehicle Design for Severe Environments 2015: 184-199 - [p2]Corina Sandu, Emmanuel Blanchard, Adrian Sandu:
Application of the Generalized Polynomial Chaos to the LQR Control Problem with Uncertain Parameters in the Formulation. Advanced Autonomous Vehicle Design for Severe Environments 2015: 200-220 - [e1]Sai Ravela, Adrian Sandu:
Dynamic Data-Driven Environmental Systems Science - First International Conference, DyDESS 2014, Cambridge, MA, USA, November 5-7, 2014, Revised Selected Papers. Lecture Notes in Computer Science 8964, Springer 2015, ISBN 978-3-319-25137-0 [contents] - [i24]Vishwas Rao, Adrian Sandu:
A Time-parallel Approach to Strong-constraint Four-dimensional Variational Data Assimilation. CoRR abs/1505.04515 (2015) - [i23]Ahmed Attia, Vishwas Rao, Adrian Sandu:
A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation. CoRR abs/1505.04724 (2015) - [i22]Vishwas Rao, Adrian Sandu, Michael Ng, Elias David Niño Ruiz:
Robust data assimilation using L1 and Huber norms. CoRR abs/1511.01593 (2015) - [i21]Azam S. Zavar Moosavi, Razvan Stefanescu, Adrian Sandu:
Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques. CoRR abs/1511.02909 (2015) - 2014
- [j29]Alexandru Cioaca, Adrian Sandu:
Low-rank approximations for computing observation impact in 4D-Var data assimilation. Comput. Math. Appl. 67(12): 2112-2126 (2014) - [j28]Mihai Alexe, Adrian Sandu:
Space-time adaptive solution of inverse problems with the discrete adjoint method. J. Comput. Phys. 270: 21-39 (2014) - [j27]Alexandru Cioaca, Adrian Sandu:
An optimization framework to improve 4D-Var data assimilation system performance. J. Comput. Phys. 275: 377-389 (2014) - [j26]Paul Tranquilli, Adrian Sandu:
Exponential-Krylov methods for ordinary differential equations. J. Comput. Phys. 278: 31-46 (2014) - [j25]Vishwas Rao, Adrian Sandu:
An adjoint-based scalable algorithm for time-parallel integration. J. Comput. Sci. 5(2): 76-84 (2014) - [j24]Hong Zhang, Adrian Sandu, Sébastien Blaise:
Partitioned and Implicit-Explicit General Linear Methods for Ordinary Differential Equations. J. Sci. Comput. 61(1): 119-144 (2014) - [j23]Angelamaria Cardone, Zdzislaw Jackiewicz, Adrian Sandu, Hong Zhang:
Extrapolation-based implicit-explicit general linear methods. Numer. Algorithms 65(3): 377-399 (2014) - [j22]Adrian Sandu:
A new look at the chemical master equation. Numer. Algorithms 65(3): 485-498 (2014) - [j21]Paul Tranquilli, Adrian Sandu:
Rosenbrock-Krylov Methods for Large Systems of Differential Equations. SIAM J. Sci. Comput. 36(3) (2014) - [j20]Hong Zhang, Adrian Sandu:
FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs. SIAM J. Sci. Comput. 36(5) (2014) - [c48]Ahmed Attia, Vishwas Rao, Adrian Sandu:
A Sampling Approach for Four Dimensional Data Assimilation. DyDESS 2014: 215-226 - [c47]Elias D. Niño, Adrian Sandu:
Variational Data Assimilation Based on Derivative-Free Optimization. DyDESS 2014: 239-250 - [c46]Vishwas Rao, Adrian Sandu:
A Posteriori Error Estimates for DDDAS Inference Problems. ICCS 2014: 1256-1265 - [i20]Paul Tranquilli, Adrian Sandu:
Lightly Implicit Krylov-Exponential (LIKE) Methods. CoRR abs/1401.2125 (2014) - [i19]Razvan Stefanescu, Adrian Sandu, Ionel Michael Navon:
Comparison of POD reduced order strategies for the nonlinear 2D Shallow Water Equations. CoRR abs/1402.2018 (2014) - [i18]Razvan Stefanescu, Adrian Sandu, Ionel Michael Navon:
POD/DEIM Strategies for reduced data assimilation systems. CoRR abs/1402.5992 (2014) - [i17]Ahmed Attia, Adrian Sandu:
A Sampling Filter for Non-Gaussian Data Assimilation. CoRR abs/1403.7137 (2014) - [i16]Elias D. Niño, Adrian Sandu:
A Derivative-Free Trust Region Framework for Variational Data Assimilation. CoRR abs/1403.7692 (2014) - [i15]Hong Zhang, Adrian Sandu, Sébastien Blaise:
High Order Implicit-Explicit General Linear Methods with Optimized Stability Regions. CoRR abs/1407.2337 (2014) - [i14]Hong Zhang, Adrian Sandu, Paul Tranquilli:
Application of approximate matrix factorization to high order linearly implicit Runge-Kutta methods. CoRR abs/1408.3622 (2014) - [i13]Razvan Stefanescu, Adrian Sandu:
Efficient approximation of sparse Jacobians for time-implicit reduced order models. CoRR abs/1409.5506 (2014) - [i12]Azam S. Zavar Moosavi, Adrian Sandu:
Approximate Exponential Algorithms to Solve the Chemical Master Equation. CoRR abs/1410.1934 (2014) - [i11]Azam S. Zavar Moosavi, Paul Tranquilli, Adrian Sandu:
Solving stochastic chemical kinetics by Metropolis Hastings sampling. CoRR abs/1410.8155 (2014) - [i10]