M. Aoki (1968):
Control of large-scale dynamic systems by aggregation.
IEEE Trans. Autom. Control 13(3),
pp. 246–253,
doi:10.1109/TAC.1968.1098900.
P. Auger, R. de la Parra, J. Poggiale, E. Sánchez & T. Nguyen-Huu (2008):
Aggregation of Variables and Applications to Population Dynamics.
In: Pierre Magal & Shigui Ruan: Structured Population Models in Biology and Epidemiology,
Lecture Notes in Mathematics 1936.
Springer,
pp. 209–263,
doi:10.1007/978-3-540-78273-5_5.
C. Baier, B. Engelen & M. E. Majster-Cederbaum (2000):
Deciding Bisimilarity and Similarity for Probabilistic Processes.
J. Comput. Syst. Sci. 60(1),
pp. 187–231,
doi:10.1006/jcss.1999.1683.
C.W. Barrett, R. Sebastiani, S.A. Seshia & C. Tinelli (2009):
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications, chapter Satisfiability Modulo Theories.
IOS Press.
N.M. Borisov, N.I. Markevich, J.B. Hoek & B.N. Kholodenko (2005):
Signaling through Receptors and Scaffolds: Independent Interactions Reduce Combinatorial Complexity.
Biophysical Journal 89(2),
pp. 951–966,
doi:10.1529/biophysj.105.060533.
F. van Breugel & J. Worrell (2006):
Approximating and computing behavioural distances in probabilistic transition systems.
Theoretical Computer Science 360(1–3),
pp. 373–385,
doi:10.1016/j.tcs.2006.05.021.
L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2015):
Forward and Backward Bisimulations for Chemical Reaction Networks.
In: 26th International Conference on Concurrency Theory, CONCUR,
pp. 226–239,
doi:10.4230/LIPIcs.CONCUR.2015.226.
L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2016):
Efficient Syntax-driven Lumping of Differential Equations.
In: Proceedings of Tools and Algorithms for the Construction and Analysis of Systems — 21st International Conference (TACAS),
doi:10.1007/978-3-662-49674-9_6.
L. Cardelli, M. Tribastone, M. Tschaikowski & A. Vandin (2016):
Symbolic computation of differential equivalences.
In: Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL,
pp. 137–150,
doi:10.1145/2837614.2837649.
L. De Moura & N. Bjørner (2008):
Z3: An Efficient SMT Solver.
In: TACAS,
pp. 337–340,
doi:10.1007/978-3-540-78800-3_24.
C. Dehnert, J.-P. Katoen & D. Parker (2013):
SMT-Based Bisimulation Minimisation of Markov Models.
In: R. Giacobazzi, J. Berdine & I. Mastroeni: Proc. 14th International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI'13),
LNCS 7737.
Springer,
pp. 28–47,
doi:10.1007/978-3-642-35873-9_5.
S. Derisavi, H. Hermanns & W.H. Sanders (2003):
Optimal state-space lumping in Markov chains.
Information Processing Letters 87(6),
pp. 309–315,
doi:10.1016/S0020-0190(03)00343-0.
J. Desharnais, V. Gupta, R. Jagadeesan & P. Panangaden (2004):
Metrics for labelled Markov processes.
Theor. Comput. Sci. 318(3),
pp. 323–354,
doi:10.1016/j.tcs.2003.09.013.
J. Feret, V. Danos, J. Krivine, R. Harmer & W. Fontana (2009):
Internal coarse-graining of molecular systems.
Proceedings of the National Academy of Sciences 106(16),
pp. 6453–6458,
doi:10.1073/pnas.0809908106.
J. Feret, T. Henzinger, H. Koeppl & T. Petrov (2012):
Lumpability abstractions of rule-based systems.
Theoretical Computer Science 431,
pp. 137–164,
doi:10.1016/j.tcs.2011.12.059.
G. Iacobelli & M. Tribastone (2013):
Lumpability of fluid models with heterogeneous agent types.
In: DSN,
pp. 1–11,
doi:10.1109/DSN.2013.6575346.
N. Israeli & N. Goldenfeld (2006):
Coarse-graining of cellular automata, emergence, and the predictability of complex systems.
Phys. Rev. E 73,
pp. 026203,
doi:10.1103/PhysRevE.73.026203.
Y. Iwasa, V. Andreasen & S. Levin (1987):
Aggregation in model ecosystems. I. Perfect aggregation.
Ecological Modelling 37(3-4),
pp. 287–302,
doi:10.1016/0304-3800(87)90030-5.
M.Z. Kwiatkowska, G. Norman & D. Parker (2011):
PRISM 4.0: Verification of Probabilistic Real-Time Systems.
In: CAV,
pp. 585–591,
doi:10.1007/978-3-642-22110-1_47.
K.G. Larsen, R. Mardare & P. Panangaden (2012):
Taking It to the Limit: Approximate Reasoning for Markov Processes.
In: MFCS,
pp. 681–692,
doi:10.1007/978-3-642-32589-2_59.
K.G. Larsen & A. Skou (1991):
Bisimulation through probabilistic testing.
Information and Computation 94(1),
pp. 1–28,
doi:10.1016/0890-5401(91)90030-6.
G. Li & H. Rabitz (1989):
A general analysis of exact lumping in chemical kinetics.
Chemical Engineering Science 44(6),
pp. 1413–1430,
doi:10.1016/0009-2509(89)85014-6.
M.S. Okino & M.L. Mavrovouniotis (1998):
Simplification of Mathematical Models of Chemical Reaction Systems.
Chemical Reviews 2(98),
pp. 391–408,
doi:10.1021/cr950223l.
R. Paige & R. Tarjan (1987):
Three Partition Refinement Algorithms.
SIAM Journal on Computing 16(6),
pp. 973–989,
doi:10.1137/0216062.
J. Toth, G. Li, H. Rabitz & A.S. Tomlin (1997):
The Effect of Lumping and Expanding on Kinetic Differential Equations.
SIAM Journal on Applied Mathematics 57(6),
pp. 1531–1556,
doi:10.1137/S0036139995293294.
M. Tribastone (2013):
A Fluid Model for Layered Queueing Networks.
IEEE Transactions on Software Engineering 39(6),
pp. 744–756,
doi:10.1109/TSE.2012.66.
M. Tribastone, S. Gilmore & J. Hillston (2012):
Scalable Differential Analysis of Process Algebra Models.
IEEE Transactions on Software Engineering 38(1),
pp. 205–219,
doi:10.1109/TSE.2010.82.
M. Tschaikowski & M. Tribastone (2015):
Approximate reduction of heterogeneous nonlinear models with differential hulls.
IEEE Transactions on Automatic Control,
doi:10.1109/TAC.2015.2457172.
T. Turanyi & A. Tomlin (2014):
Analysis of Kinetic Reaction Mechanisms.
Springer,
doi:10.1007/978-3-662-44562-4.
A. Valmari & G. Franceschinis (2010):
Simple O(m logn) Time Markov Chain Lumping.
In: TACAS,
pp. 38–52,
doi:10.1007/978-3-642-12002-2_4.
Eberhard O. Voit (2013):
Biochemical Systems Theory: A Review.
ISRN Biomathematics 2013,
pp. 53,
doi:10.1155/2013/897658.
eptcs@eptcs.org