scholarly journals Real Analysis for Complex Systems

10.29007/ksvj ◽  
2018 ◽  
Author(s):  
Andrè Platzer

Formal verification techniques are used routinely in finite-state digital circuits. Theorem proving is also used successfully for infinite-state discrete systems. But many safety-critical computers are actually embedded in physical systems. Hybrid systems model complex physical systems as dynamical systems with interacting discrete transitions and continuous evolutions along differential equations. They arise frequently in many application domains, including aviation, automotive, railway, and robotics. There is a well-understood theory for proving programs. But what about complex physical systems? How can we prove that a hybrid system works as expected, e.g., an aircraft does not crash into another one?This talk illustrates the complexities and pitfalls of hybrid systems verification. It describes a theoretical and practical foundation for deductive verification of hybrid systems called differential dynamic logic. The proof calculus for this logic is interesting from a theoretical perspective, because it is a complete axiomatization of hybrid systems relative to differential equations. The approach is of considerable practical interest too. Its implementation in the theorem prover KeYmaera has been used successfully to verify collision avoidance properties in the European Train Control System and air traffic control systems. The number of dimensions and nonlinearities in they hybrid dynamics of these systems is surprisingly tricky such that they are still out of scope for other verification tools.

Author(s):  
Yong Kiam Tan ◽  
André Platzer

AbstractThis article presents an axiomatic approach for deductive verification of existence and liveness for ordinary differential equations (ODEs) with differential dynamic logic (dL). The approach yields proofs that the solution of a given ODE exists long enough to reach a given target region without leaving a given evolution domain. Numerous subtleties complicate the generalization of discrete liveness verification techniques, such as loop variants, to the continuous setting. For example, ODE solutions may blow up in finite time or their progress towards the goal may converge to zero. These subtleties are handled in dL by successively refining ODE liveness properties using ODE invariance properties which have a complete axiomatization. This approach is widely applicable: several liveness arguments from the literature are surveyed and derived as special instances of axiomatic refinement in dL. These derivations also correct several soundness errors in the surveyed literature, which further highlights the subtlety of ODE liveness reasoning and the utility of an axiomatic approach. An important special case of this approach deduces (global) existence properties of ODEs, which are a fundamental part of every ODE liveness argument. Thus, all generalizations of existence properties and their proofs immediately lead to corresponding generalizations of ODE liveness arguments. Overall, the resulting library of common refinement steps enables both the sound development and justification of new ODE existence and of liveness proof rules from dL axioms. These insights are put into practice through an implementation of ODE liveness proofs in the KeYmaera X theorem prover for hybrid systems.


2011 ◽  
Vol 8 (2) ◽  
pp. 7-10 ◽  
Author(s):  
Lei Bu ◽  
Qixin Wang ◽  
Xin Chen ◽  
Linzhang Wang ◽  
Tian Zhang ◽  
...  

Author(s):  
Cécile Penland ◽  
Brian D Ewald

Stochastic descriptions of multiscale interactions are more and more frequently found in numerical models of weather and climate. These descriptions are often made in terms of differential equations with random forcing components. In this article, we review the basic properties of stochastic differential equations driven by classical Gaussian white noise and compare with systems described by stable Lévy processes. We also discuss aspects of numerically generating these processes.


2021 ◽  
Author(s):  
Mrinal Kanti Das ◽  
Lal Mohan Saha

Emergence of chaos and complex behavior in real and physical systems has been discussed within the framework of nonlinear dynamical systems. The problems investigated include complexity of Child’s swing dynamics , chaotic neuronal dynamics (FHN model), complex Food-web dynamics, Financial model (involving interest rate, investment demand and price index) etc. Proper numerical simulations have been carried out to unravel the complex dynamics of these systems and significant results obtained are displayed through tables and various plots like bifurcations, attractors, Lyapunov exponents, topological entropies, correlation dimensions, recurrence plots etc. The significance of artificial neural network (ANN) framework for time series generation of some dynamical system is suggested.


2015 ◽  
Vol 25 (14) ◽  
pp. 1540024 ◽  
Author(s):  
Marat Akhmet ◽  
Mehmet Onur Fen

By using the reduction technique to impulsive differential equations [Akhmet & Turan, 2006], we rigorously prove the presence of chaos in dynamic equations on time scales (DETS). The results of the present study are based on the Li–Yorke definition of chaos. This is the first time in the literature that chaos is obtained for DETS. An illustrative example is presented by means of a Duffing equation on a time scale.


2012 ◽  
Vol 11 ◽  
pp. CIN.S8185 ◽  
Author(s):  
Xiangfang Li ◽  
Lijun Qian ◽  
Michale L. Bittner ◽  
Edward R. Dougherty

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug's pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.


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