Andreas Hellander

Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Zhang, Tianru; Gupta, Ankit; Francisco Rodríguez, María Andreína; Spjuth, Ola et al.

Part of Expert systems with applications, p. 121443-121443, 2024

• DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Accelerating Fair Federated Learning: Adaptive Federated Adam

Ju, Li; Zhang, Tianru; Toor, Salman; Hellander, Andreas

Part of IEEE Transactions on Machine Learning in Communications and Networking, p. 1017-1032, 2024

• DOI for Accelerating Fair Federated Learning: Adaptive Federated Adam
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Federated learning for predicting compound mechanism of action based on image-data from cell painting

Ju, Li; Hellander, Andreas; Spjuth, Ola

Part of Artificial Intelligence in the Life Sciences, 2024

• DOI for Federated learning for predicting compound mechanism of action based on image-data from cell painting
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Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Zhang, Tianru; Gupta, Ankit; Francisco Rodríguez, María Andreína; Spjuth, Ola et al.

Part of Expert systems with applications, 2024

• DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system
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Toward efficient resource utilization at edge nodes in federated learning

Alawadi, Sadi; Ait-Mlouk, Addi; Toor, Salman; Hellander, Andreas

Part of Progress in Artificial Intelligence, p. 101-117, 2024

• DOI for Toward efficient resource utilization at edge nodes in federated learning
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Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Zhang, Tianru; Gupta, Ankit; Francisco Rodríguez, María Andreína; Spjuth, Ola et al.

Part of Expert systems with applications, p. 121443-121443, 2024

• DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Accelerating Fair Federated Learning: Adaptive Federated Adam

Ju, Li; Zhang, Tianru; Toor, Salman; Hellander, Andreas

Part of IEEE Transactions on Machine Learning in Communications and Networking, p. 1017-1032, 2024

• DOI for Accelerating Fair Federated Learning: Adaptive Federated Adam
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Federated learning for predicting compound mechanism of action based on image-data from cell painting

Ju, Li; Hellander, Andreas; Spjuth, Ola

Part of Artificial Intelligence in the Life Sciences, 2024

• DOI for Federated learning for predicting compound mechanism of action based on image-data from cell painting
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Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Zhang, Tianru; Gupta, Ankit; Francisco Rodríguez, María Andreína; Spjuth, Ola et al.

Part of Expert systems with applications, 2024

• DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system
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Toward efficient resource utilization at edge nodes in federated learning

Alawadi, Sadi; Ait-Mlouk, Addi; Toor, Salman; Hellander, Andreas

Part of Progress in Artificial Intelligence, p. 101-117, 2024

• DOI for Toward efficient resource utilization at edge nodes in federated learning
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Efficient Hierarchical Storage Management Empowered by Reinforcement Learning

Zhang, Tianru; Hellander, Andreas; Toor, Salman

Part of IEEE Transactions on Knowledge and Data Engineering, p. 5780-5793, 2023

• DOI for Efficient Hierarchical Storage Management Empowered by Reinforcement Learning

Contributions of cell behavior to geometric order in embryonic cartilage

Mathias, Sonja; Adameyko, Igor; Hellander, Andreas; Kursawe, Jochen

Part of PloS Computational Biology, 2023

• DOI for Contributions of cell behavior to geometric order in embryonic cartilage
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CBMOS: a GPU-enabled Python framework for the numerical study of center-based models

Mathias, Sonja; Coulier, Adrien; Hellander, Andreas

Part of BMC Bioinformatics, 2022

• DOI for CBMOS: a GPU-enabled Python framework for the numerical study of center-based models
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FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning

Ait-Mlouk, Addi; Alawadi, Sadi; Toor, Salman; Hellander, Andreas

Part of Applied Sciences, 2022

• DOI for FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
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Living in darkness: Exploring adaptation of Proteus anguinus in 3 dimensions by X-ray imaging

Tesarova, Marketa; Mancini, Lucia; Mauri, Edgardo; Aljancic, Gregor et al.

Part of GigaScience, 2022

• DOI for Living in darkness: Exploring adaptation of Proteus anguinus in 3 dimensions by X-ray imaging
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Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods

Jiang, Richard; Singh, Prashant; Wrede, Fredrik; Hellander, Andreas et al.

Part of PloS Computational Biology, 2022

• DOI for Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods
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Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation

Åkesson, Mattias; Singh, Prashant; Wrede, Fredrik; Hellander, Andreas

Part of IEEE/ACM Transactions on Computational Biology & Bioinformatics, p. 3353-3365, 2022

• DOI for Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation

Altered developmental programs and oriented cell divisions lead to bulky bones during salamander limb regeneration

Kaucka, Marketa; Araus, Alberto Joven; Tesarova, Marketa; Currie, Joshua D. et al.

Part of Nature Communications, 2022

• DOI for Altered developmental programs and oriented cell divisions lead to bulky bones during salamander limb regeneration
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Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation

Coulier, Adrien; Singh, Prashant; Sturrock, Marc; Hellander, Andreas

Part of PloS Computational Biology, 2022

• DOI for Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation
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Surface flow for colonial integration in reef-building corals

Bouderlique, Thibault; Petersen, Julian; Faure, Louis; Abed-Navandi, Daniel et al.

Part of Current Biology, p. 2596-2609, 2022

• DOI for Surface flow for colonial integration in reef-building corals
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Approaches for containerized scientific workflows in cloud environments with applications in life science

Spjuth, Ola; Capuccini, Marco; Carone, Matteo; Larsson, Anders et al.

Part of F1000 Research, p. 513-513, 2021

• DOI for Approaches for containerized scientific workflows in cloud environments with applications in life science

A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis

Coulier, Adrien; Hellander, Stefan; Hellander, Andreas

Part of Journal of Chemical Physics, 2021

• DOI for A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
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Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators

Jiang, Richard M.; Wrede, Fredrik; Singh, Prashant; Hellander, Andreas et al.

Part of BMC Bioinformatics, 2021

• DOI for Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators
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Epidemiological modeling in StochSS Live!

Jiang, Richard; Jacob, Bruno; Geiger, Matthew; Matthew, Sean et al.

Part of Bioinformatics, p. 2787-2788, 2021

• DOI for Epidemiological modeling in StochSS Live!

Scalable machine learning-assisted model exploration and inference using Sciope

Singh, Prashant; Wrede, Fredrik; Hellander, Andreas

Part of Bioinformatics, p. 279-281, 2021

• DOI for Scalable machine learning-assisted model exploration and inference using Sciope
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The machine learning life cycle and the cloud: implications for drug discovery.

Spjuth, Ola; Frid, Jens; Hellander, Andreas

Part of Expert Opinion on Drug Discovery, p. 1071-1079, 2021

• DOI for The machine learning life cycle and the cloud: implications for drug discovery.

Deep-learning models for lipid nanoparticle-based drug delivery

Harrison, Philip J.; Wieslander, Håkan; Sabirsh, Alan; Karlsson, Johan et al.

Part of Nanomedicine, p. 1097-1110, 2021

• DOI for Deep-learning models for lipid nanoparticle-based drug delivery

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Blamey, Ben; Toor, Salman; Dahlö, Martin; Wieslander, Håkan et al.

Part of GigaScience, p. 1-14, 2021

• DOI for Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
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Impact of Force Function Formulations on the Numerical Simulation of Centre-Based Models.

Mathias, Sonja; Coulier, Adrien; Bouchnita, Anass; Hellander, Andreas

Part of Bulletin of Mathematical Biology, 2020

• DOI for Impact of Force Function Formulations on the Numerical Simulation of Centre-Based Models.
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Hierarchical algorithm for the reaction–diffusion master equation

Hellander, Stefan; Hellander, Andreas

Part of Journal of Chemical Physics, 2020

• DOI for Hierarchical algorithm for the reaction–diffusion master equation

A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma

Bouchnita, Anass; Volpert, Vitaly; Koury, Mark J.; Hellander, Andreas

Part of Mathematical Biosciences, 2020

• DOI for A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma

Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning

Wrede, Fredrik; Hellander, Andreas

Part of Bioinformatics, p. 5199-5206, 2019

• DOI for Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning

A 3D multiscale model to explore the role of EGFR overexpression in tumourigenesis

Bouchnita, Anass; Hellander, Stefan; Hellander, Andreas

Part of Bulletin of Mathematical Biology, p. 2323-2344, 2019

• DOI for A 3D multiscale model to explore the role of EGFR overexpression in tumourigenesis

BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data

Ausmees, Kristiina; John, Aji; Toor, Salman Z.; Hellander, Andreas et al.

Part of BMC Bioinformatics, 2018

• DOI for BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data

Robustness analysis of spatiotemporal models in the presence of extrinsic fluctuations

Hellander, Andreas; Klosa, Jan; Lötstedt, Per; MacNamara, Shev

Part of SIAM Journal on Applied Mathematics, p. 1157-1183, 2017

• DOI for Robustness analysis of spatiotemporal models in the presence of extrinsic fluctuations

Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning

Hellander, Stefan; Hellander, Andreas; Petzold, Linda

Part of Journal of Chemical Physics, 2017

• DOI for Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning
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Oriented clonal cell dynamics enables accurate growth and shaping of vertebrate cartilage

Kaucka, Marketa; Zikmund, Tomas; Tesarova, Marketa; Gyllborg, Daniel et al.

Part of eLIFE, 2017

• DOI for Oriented clonal cell dynamics enables accurate growth and shaping of vertebrate cartilage

GillesPy: A Python package for stochastic model building and simulation

Abel, John H.; Drawert, Brian; Hellander, Andreas; Petzold, Linda R.

Part of IEEE Life Sciences Letters, p. 35-38, 2016

• DOI for GillesPy: A Python package for stochastic model building and simulation

Stochastic Simulation Service: Bridging the gap between the computational expert and the biologist

Drawert, Brian; Hellander, Andreas; Bales, Ben; Banerjee, Debjani et al.

Part of PloS Computational Biology, 2016

• DOI for Stochastic Simulation Service: Bridging the gap between the computational expert and the biologist

MOLNs: A cloud platform for interactive, reproducible, and scalable spatial stochastic computational experiments in systems biology using PyURDME

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda et al.

Part of SIAM Journal on Scientific Computing, 2016

• DOI for MOLNs: A cloud platform for interactive, reproducible, and scalable spatial stochastic computational experiments in systems biology using PyURDME

Mesoscopic modeling of stochastic reaction–diffusion kinetics in the subdiffusive regime

Blanc, Emilie; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per

Part of Multiscale Modeling & simulation, p. 668-707, 2016

• DOI for Mesoscopic modeling of stochastic reaction–diffusion kinetics in the subdiffusive regime

Analysis and design of jump coefficients in discrete stochastic diffusion models

Meinecke, Lina; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per

Part of SIAM Journal on Scientific Computing, 2016

• DOI for Analysis and design of jump coefficients in discrete stochastic diffusion models
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Analysis of neural crest-derived clones reveals novel aspects of facial development

Kaucka, Marketa; Ivashkin, Evgeny; Gyllborg, Daniel; Zikmund, Tomas et al.

Part of Science Advances, 2016

• DOI for Analysis of neural crest-derived clones reveals novel aspects of facial development

Reaction rates for mesoscopic reaction-diffusion kinetics

Hellander, Stefan; Hellander, Andreas; Petzold, Linda

Part of Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 2015

• DOI for Reaction rates for mesoscopic reaction-diffusion kinetics
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Accuracy of the Michaelis–Menten approximation when analysing effects of molecular noise

Lawson, Michael J.; Petzold, Linda; Hellander, Andreas

Part of Journal of the Royal Society Interface, 2015

• DOI for Accuracy of the Michaelis–Menten approximation when analysing effects of molecular noise
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Local error estimates for adaptive simulation of the reaction–diffusion master equation via operator splitting

Hellander, Andreas; Lawson, Michael J.; Drawert, Brian; Petzold, Linda

Part of Journal of Computational Physics, p. 89-100, 2014

• DOI for Local error estimates for adaptive simulation of the reaction–diffusion master equation via operator splitting

Coupled mesoscopic and microscopic simulation of stochastic reaction-diffusion processes in mixed dimensions

Hellander, Andreas; Hellander, Stefan; Lötstedt, Per

Part of Multiscale Modeling & simulation, p. 585-611, 2012

• DOI for Coupled mesoscopic and microscopic simulation of stochastic reaction-diffusion processes in mixed dimensions

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

Part of BMC Systems Biology, 2012

• DOI for URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries

Reaction-diffusion master equation in the microscopic limit

Hellander, Stefan; Hellander, Andreas; Petzold, Linda

Part of Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 2012

• DOI for Reaction-diffusion master equation in the microscopic limit

CellMC: a multiplatform model compiler for the Cell Broadband Engine and x86

Caulfield, Emmet; Hellander, Andreas

Part of Bioinformatics, p. 426-428, 2010

• DOI for CellMC: a multiplatform model compiler for the Cell Broadband Engine and x86

Incorporating active transport of cellular cargo in stochastic mesoscopic models of living cells

Hellander, Andreas; Lötstedt, Per

Part of Multiscale Modeling & simulation, p. 1691-1714, 2010

• DOI for Incorporating active transport of cellular cargo in stochastic mesoscopic models of living cells

An adaptive algorithm for simulation of stochastic reaction-diffusion processes

Ferm, Lars; Hellander, Andreas; Lötstedt, Per

Part of Journal of Computational Physics, p. 343-360, 2010

• DOI for An adaptive algorithm for simulation of stochastic reaction-diffusion processes

Simulation of stochastic reaction-diffusion processes on unstructured meshes

Engblom, Stefan; Ferm, Lars; Hellander, Andreas; Lötstedt, Per

Part of SIAM Journal on Scientific Computing, p. 1774-1797, 2009

• DOI for Simulation of stochastic reaction-diffusion processes on unstructured meshes

Sparse grids and hybrid methods for the chemical master equation

Hegland, Markus; Hellander, Andreas; Lötstedt, Per

Part of BIT Numerical Mathematics, p. 265-283, 2008

• DOI for Sparse grids and hybrid methods for the chemical master equation

Efficient computation of transient solutions of the chemical master equation based on uniformization and quasi-Monte Carlo

Hellander, Andreas

Part of Journal of Chemical Physics, 2008

• DOI for Efficient computation of transient solutions of the chemical master equation based on uniformization and quasi-Monte Carlo

A hierarchy of approximations of the master equation scaled by a size parameter

Ferm, Lars; Lötstedt, Per; Hellander, Andreas

Part of Journal of Scientific Computing, p. 127-151, 2008

• DOI for A hierarchy of approximations of the master equation scaled by a size parameter

Hybrid method for the chemical master equation

Hellander, Andreas; Lötstedt, Per

Part of Journal of Computational Physics, p. 100-122, 2007

• DOI for Hybrid method for the chemical master equation

Multiscale simulation of stochastic reaction–diffusion networks

Engblom, Stefan; Hellander, Andreas; Lötstedt, Per

Part of Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, p. 55-79, Springer, 2017

• DOI for Multiscale simulation of stochastic reaction–diffusion networks

Multiscale Stochastic Simulation of Reaction-Transport Processes: Applications in Molecular Systems Biology

Hellander, Andreas

2011

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Numerical simulation of well stirred biochemical reaction networks governed by the master equation

Hellander, Andreas

2008

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Efficient Hierarchical Storage Management Empowered by Reinforcement Learning Extended Abstract

Zhang, Tianru; Hellander, Andreas; Toor, Salman

p. 3869-3870, 2023

• DOI for Efficient Hierarchical Storage Management Empowered by Reinforcement Learning Extended Abstract

Robust and integrative Bayesian neural networks for likelihood-free parameter inference

Wrede, Fredrik; Eriksson, Robin; Jiang, Richard; Petzold, Linda et al.

Part of 2022 International Joint Conference on Neural Networks (IJCNN), p. 1-10, 2022

• DOI for Robust and integrative Bayesian neural networks for likelihood-free parameter inference

Scalable federated machine learning with FEDn

Ekmefjord, Morgan; Ait-Mlouk, Addi; Alawadi, Sadi; Akesson, Mattias et al.

Part of 2022 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022), p. 555-564, 2022

• DOI for Scalable federated machine learning with FEDn

Smart Resource Management for Data Streaming using an Online Bin-packing Strategy

Stein, Oliver; Blamey, Ben; Karlsson, Johan; Sabirsh, Alan et al.

Part of 2020 IEEE International Conference on Big Data (Big Data), p. 2207-2216, 2020

• DOI for Smart Resource Management for Data Streaming using an Online Bin-packing Strategy

Apache Spark Streaming, Kafka and HarmonicIO: A performance benchmark and architecture comparison for enterprise and scientific computing

Blamey, Ben; Hellander, Andreas; Toor, Salman

Part of Benchmarking, Measuring, and Optimizing, p. 335-347, 2020

• DOI for Apache Spark Streaming, Kafka and HarmonicIO: A performance benchmark and architecture comparison for enterprise and scientific computing

Adapting the secretary hiring problem for optimal hot–cold tier placement under top-K workloads

Blamey, Ben; Wrede, Fredrik; Karlsson, Johan; Hellander, Andreas et al.

Part of Proc. 19th International Symposium on Cluster, Cloud, and Grid Computing, p. 576-583, 2019

• DOI for Adapting the secretary hiring problem for optimal hot–cold tier placement under top-K workloads

Orchestral: a lightweight framework for parallel simulations of cell–cell communication

Coulier, Adrien; Hellander, Andreas

Part of Proc. 14th International Conference on e-Science, p. 168-176, 2018

• DOI for Orchestral: a lightweight framework for parallel simulations of cell–cell communication

Hyperparameter optimization for approximate Bayesian computation

Singh, Prashant; Hellander, Andreas

Part of Proc. 50th Winter Simulation Conference, p. 1718-1729, 2018

• DOI for Hyperparameter optimization for approximate Bayesian computation

HarmonicIO: Scalable data stream processing for scientific datasets

Torruangwatthana, Preechakorn; Wieslander, Håkan; Blamey, Ben; Hellander, Andreas et al.

Part of Proc. 11th International Conference on Cloud Computing, p. 879-882, 2018

• DOI for HarmonicIO: Scalable data stream processing for scientific datasets

Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference

Singh, Prashant; Hellander, Andreas

Part of Proc. 32nd European Simulation and Modelling Conference, p. 22-27, 2018

Cost-aware application development and management using CLOUD-METRIC

Jallow, Alieu; Hellander, Andreas; Toor, Salman

Part of Proc. 7th International Conference on Cloud Computing and Services Science, p. 515-522, 2017

• DOI for Cost-aware application development and management using CLOUD-METRIC

Surrogate assisted model reduction for stochastic biochemical reaction networks

Singh, Prashant; Hellander, Andreas

Part of Proc. 49th Winter Simulation Conference, p. 1773-1783, 2017

• DOI for Surrogate assisted model reduction for stochastic biochemical reaction networks

SNIC Science Cloud (SSC): A national-scale cloud infrastructure for Swedish academia

Toor, Salman; Lindberg, Mathias; Fällman, Ingemar; Vallin, Andreas et al.

Part of Proc. 13th International Conference on e-Science, p. 219-227, 2017

• DOI for SNIC Science Cloud (SSC): A national-scale cloud infrastructure for Swedish academia

Automatic and portable cloud deployment for scientific simulations

Horuk, Christopher B.; Douglas, Geoffrey; Gupta, Anand; Krintz, Chandra et al.

Part of Proc. 12th International Conference on High Performance Computing and Simulation, p. 374-381, 2014

• DOI for Automatic and portable cloud deployment for scientific simulations

Scientific analysis by queries in extended SPARQL over a scalable e-Science data store

Andrejev, Andrej; Toor, Salman; Hellander, Andreas; Holmgren, Sverker et al.

Part of Proc. 9th International Conference on e-Science, p. 98-106, 2013

• DOI for Scientific analysis by queries in extended SPARQL over a scalable e-Science data store

Abstractions for scaling eScience applications to distributed computing environments: A StratUm integration case study in molecular systems biology

Östberg, Per-Olov; Hellander, Andreas; Drawert, Brian; Elmroth, Erik et al.

Part of Proc. 3rd International Conference on Bioinformatics Models, Methods and Algorithms, p. 290-294, 2012

• DOI for Abstractions for scaling eScience applications to distributed computing environments: A StratUm integration case study in molecular systems biology

Reducing complexity in management of eScience computations

Östberg, Per-Olov; Hellander, Andreas; Drawert, Brian; Elmroth, Erik et al.

Part of Proc. 12th International Symposium on Cluster, Cloud and Grid Computing, p. 845-852, 2012

• DOI for Reducing complexity in management of eScience computations

Adaptive stochastic hybrid simulation of biochemical reaction-diffusion models

Hellander, Andreas; Ferm, Lars; Lötstedt, Per

Part of Proc. 3rd International Conference on Foundations of Systems Biology in Engineering, p. 4, 2009

A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation

Coulier, Adrien; Singh, Prashant; Sturrock, Marc; Hellander, Andreas

Coupled mesoscopic and microscopic simulation of stochastic reaction-diffusion processes in mixed dimensions

Hellander, Andreas; Hellander, Stefan; Lötstedt, Per

2011

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Incorporating active transport of cellular cargo in stochastic mesoscopic models of living cells

Hellander, Andreas; Lötstedt, Per

2010

• Download full text (pdf) of Incorporating active transport of cellular cargo in stochastic mesoscopic models of living cells

An adaptive algorithm for simulation of stochastic reaction-diffusion processes

Ferm, Lars; Hellander, Andreas; Lötstedt, Per

2009

• Download full text (pdf) of An adaptive algorithm for simulation of stochastic reaction-diffusion processes

Simulation of stochastic reaction-diffusion processes on unstructured meshes

Engblom, Stefan; Ferm, Lars; Hellander, Andreas; Lötstedt, Per

2008

• Download full text (pdf) of Simulation of stochastic reaction-diffusion processes on unstructured meshes

Efficient computation of transient solutions of the chemical master equation based on uniformization and Quasi-Monte Carlo

Hellander, Andreas

2007

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A hierarchy of approximations of the master equation scaled by a size parameter

Ferm, Lars; Lötstedt, Per; Hellander, Andreas

2007

• Download full text (pdf) of A hierarchy of approximations of the master equation scaled by a size parameter

Hybrid method for the chemical master equation

Hellander, Andreas; Lötstedt, Per

2006

• Download full text (pdf) of Hybrid method for the chemical master equation