Christian Schilling

Christian Schilling
Aalborg University · Department of Computer Science

PhD in Computer Science

About

60
Publications
3,843
Reads
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661
Citations
Citations since 2016
46 Research Items
643 Citations
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
2016201720182019202020212022050100150200250
Introduction
Christian Schilling currently works at Aalborg University.

Publications

Publications (60)
Chapter
We propose an algorithmic approach for synthesizing linear hybrid automata from time-series data. Unlike existing approaches, our approach provides a whole family of models with the same discrete structure but different dynamics. Each model in the family is guaranteed to capture the input data up to a precision error ε, in the following sense: For...
Preprint
Full-text available
We propose an algorithmic approach for synthesizing linear hybrid automata from time-series data. Unlike existing approaches, our approach provides a whole family of models. Each model in the family is guaranteed to capture the input data up to a precision error {\epsilon}, in the following sense: For each time series, the model contains an executi...
Article
We study the verification problem for closed-loop dynamical systems with neural-network controllers (NNCS). This problem is commonly reduced to computing the set of reachable states. When considering dynamical systems and neural networks in isolation, there exist precise approaches for that task based on set representations respectively called Tayl...
Chapter
We present the first review of methods to overapproximate the set of reachable states of linear time-invariant systems subject to uncertain initial states and input signals for short time horizons. These methods are fundamental to state-of-the-art reachability algorithms for long time horizons, which proceed in two steps: First they use such a meth...
Article
Approximating the set of reachable states of a dynamical system is an algorithmic way to rigorously reason about its safety. Despite progress on efficient algorithms for affine dynamical systems, available algorithms still lack scalability to ensure their wide adoption in practice. While modern linear algebra packages are efficient for matrices wit...
Preprint
Full-text available
We study the verification problem for closed-loop dynamical systems with neural-network controllers (NNCS). This problem is commonly reduced to computing the set of reachable states. When considering dynamical systems and neural networks in isolation, there exist precise approaches for that task based on set representations respectively called Tayl...
Preprint
Full-text available
We present the first review of methods to overapproximate the set of reachable states of linear time-invariant systems subject to uncertain initial states and input signals for short time horizons. These methods are fundamental to state-of-the-art reachability algorithms for long time horizons, which proceed in two steps: they first use such a meth...
Chapter
Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. The typical approach is to detect inputs from novel classes and retrain the...
Preprint
Full-text available
LazySets.jl is a Julia library that provides ways to symbolically represent sets of points as geometric shapes, with a special focus on convex sets and polyhedral approximations. LazySets provides methods to apply common set operations, convert between different set representations, and efficiently compute with sets in high dimensions using special...
Chapter
In this article we introduce a solution method for a special class of nonlinear initial-value problems using set-based propagation techniques. The novelty of the approach is that we employ a particular embedding (Carleman linearization) to leverage recent advances of high-dimensional reachability solvers for linear ordinary differential equations b...
Preprint
Full-text available
In this article we introduce a solution method for a special class of nonlinear initial-value problems using set-based propagation techniques. The novelty of the approach is that we employ a particular embedding (Carleman linearization) to leverage recent advances of high-dimensional reachability solvers for linear ordinary differential equations b...
Preprint
Full-text available
Formal design of embedded and cyber-physical systems relies on mathematical modeling. In this paper, we consider the model class of hybrid automata whose dynamics are defined by affine differential equations. Given a set of time-series data, we present an algorithmic approach to synthesize a hybrid automaton exhibiting behavior that is close to the...
Article
Reachability analysis aims at identifying states reachable by a system within a given time horizon. This task is known to be computationally expensive for linear hybrid systems. Reachability analysis works by iteratively applying continuous and discrete post operators to compute states reachable according to continuous and discrete dynamics, respec...
Preprint
Full-text available
Machine-learning techniques achieve excellent performance in modern applications. In particular, neural networks enable training classifiers, often used in safety-critical applications, to complete a variety of tasks without human supervision. Neural-network models have neither the means to identify what they do not know nor to interact with the hu...
Preprint
Full-text available
Efficiently handling time-triggered and possibly nondeterministic switches for hybrid systems reachability is a challenging task. In this paper we present an approach based on conservative set-based enclosure of the dynamics that can handle systems with uncertain parameters and inputs, where the uncertainties are bound to given intervals. The metho...
Preprint
Full-text available
Neural networks have demonstrated unmatched performance in a range of classification tasks. Despite numerous efforts of the research community, novelty detection remains one of the significant limitations of neural networks. The ability to identify previously unseen inputs as novel is crucial for our understanding of the decisions made by neural ne...
Chapter
We present two algorithmic approaches for synthesizing linear hybrid automata from experimental data. Unlike previous approaches, our algorithms work without a template and generate an automaton with nondeterministic guards and invariants, and with an arbitrary number and topology of modes. They thus construct a succinct model from the data and pro...
Preprint
Full-text available
Reachability analysis aims at identifying states reachable by a system within a given time horizon. This task is known to be computationally hard for hybrid systems. One of the main challenges is the handling of discrete transitions, including computation of intersections with invariants and guards. In this paper, we address this problem by proposi...
Chapter
Static program analyzers are increasingly effective in checking correctness properties of programs and reporting any errors found, often in the form of error traces. However, developers still spend a significant amount of time on debugging. This involves processing long error traces in an effort to localize a bug to a relatively small part of the p...
Article
Full-text available
Hybrid automata are an important formalism for modeling dynamical systems exhibiting mixed discrete–continuous behavior such as control systems and are amenable to formal verification. However, hybrid automata lack expressiveness compared to integrated model-based design frameworks such as the MathWorks’ Simulink/Stateflow (SlSf). In this paper, we...
Preprint
Full-text available
We present JuliaReach, a toolbox for set-based reachability analysis of dynamical systems. JuliaReach consists of two main packages: Reachability, containing implementations of reachability algorithms for continuous and hybrid systems, and LazySets, a standalone library that implements state-of-the-art algorithms for calculus with convex sets. The...
Chapter
Full-text available
Ultimate Taipan is a software model checker that uses trace abstraction and abstract interpretation to prove correctness of programs. In contrast to previous versions, Ultimate Taipan now uses dynamic block encoding to obtain the best precision possible when evaluating transition formulas of large block encoded programs.
Chapter
Full-text available
Ultimate Automizer is a software verifier that generalizes proofs for traces to proofs for larger parts for the program. In recent years the portfolio of proof producers that are available to Ultimate has grown continuously. This is not only because more trace analysis algorithms have been implemented in Ultimate but also due to the continuous prog...
Conference Paper
Approximating the set of reachable states of a dynamical system is an algorithmic yet mathematically rigorous way to reason about its safety. Although progress has been made in the development of efficient algorithms for affine dynamical systems, available algorithms still lack scalability to ensure their wide adoption in the industrial setting. Wh...
Article
Full-text available
Approximating the set of reachable states of a dynamical system is an algorithmic yet mathematically rigorous way to reason about its safety. Although progress has been made in the development of efficient algorithms for affine dynamical systems, available algorithms still lack scalability to ensure their wide adoption in the industrial setting. Wh...
Thesis
Full-text available
This thesis introduces three fundamental techniques for the analysis of systems. A new algorithm for the automatic parameter synthesis in stochastic systems. The algorithm performs a search in the parameter domain and integrates precise but slow stochastic simulation and less precise but fast approximation techniques. During the search, the algori...
Article
Ultimate Taipan is a software model checker that uses trace abstraction and abstract interpretation to prove correctness of programs. In contrast to previous versions, Ultimate Taipan now uses dynamic block encoding to obtain the best precision possible when evaluating transition formulas of large block encoded programs.
Article
Ultimate Automizer is a software verifier that generalizes proofs for traces to proofs for larger parts for the program. In recent years the portfolio of proof producers that are available to Ultimate has grown continuously. This is not only because more trace analysis algorithms have been implemented in Ultimate but also due to the continuous prog...
Conference Paper
Full-text available
In this paper, we propose an approach to automatically compute invariant clusters for nonlinear semialgebraic hybrid systems. An invariant cluster for an ordinary differential equation (ODE) is a multivariate polynomial invariant g(u, x)=0, parametric in u, which can yield an infinite number of concrete invariants by assigning different values to u...
Conference Paper
Ultimate Automizer is a software verifier that implements an automata-based approach for the verification of safety and liveness properties. A central new feature that speeded up the abstraction refinement of the tool is an on-demand construction of Floyd-Hoare automata.
Conference Paper
We consider the problem of state-space reduction for nondeterministic weakly-hierarchical visibly pushdown automata (Vpa). Vpa recognize a robust and algorithmically tractable fragment of context-free languages that is natural for modeling programs. We define an equivalence relation that is sufficient for language-preserving quotienting of Vpa. Our...
Conference Paper
Ultimate Taipan is a software model checker for C programs. It is based on a CEGAR variant, trace abstraction [7], where program abstractions, counterexample selection and abstraction refinement are based on automata. Ultimate Taipan constructs path programs from counterexamples and computes fixpoints for those path programs using abstract interpre...
Article
Full-text available
We consider the problem of state-space reduction for nondeterministic weakly-hierarchical visibly pushdown automata (VPA). VPA recognize a robust and algorithmically tractable fragment of context-free languages that is natural for modeling programs. We define an equivalence relation that is sufficient for language-preserving quotienting of VPA. Our...
Conference Paper
Full-text available
Many biological systems can be modeled as multiaffine hybrid systems. Due to the nonlinearity of multiaffine systems, it is difficult to verify their properties of interest directly. A common strategy to tackle this problem is to construct and analyze a discrete overapproximation of the original system. However, the conservativeness of a discrete a...
Article
Full-text available
PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent work has shown that it can be modeled as a network of HAs. Addressing the complexity of nonlinear PDDL+ planning as HAs requires both space and time efficient reasoning. Unfortunately, existing solvers either do not address nonlinear dynamics or do not natively support netwo...
Conference Paper
Full-text available
Hybrid systems have both continuous and discrete dynamics and are useful for modeling a variety of control systems, from air traffic control protocols to robotic maneuvers and beyond. Recently, numerous powerful and scalable tools for analyzing hybrid systems have emerged. Several of these tools implement automated formal methods for mathematically...
Article
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate represent...
Conference Paper
Full-text available
Hybrid automata are an important formalism for modeling dynamical systems exhibiting mixed discrete-continuous behavior such as control systems and are amenable to formal verification. However, hybrid automata lack expressiveness compared to integrated model-based design (MBD) frameworks such as the MathWorks’ Simulink/Stateflow (SLSF). In this pap...
Conference Paper
Full-text available
In this paper, we propose an approach to automatically compute invariant clusters for semialgebraic hybrid systems. An invariant cluster for an ordinary differential equation (ODE) is a multivariate polynomial invariant g(u,x)=0, parametric in u, which can yield an infinite number of concrete invariants by assigning different values to u so that ev...
Conference Paper
Full-text available
Hybrid systems play an important role in many application domains. A range of powerful analysis methods for this class of systems perform high-level analysis, where, iteratively, (1) a reachability computation is performed on a system model, (2) the result of the analysis is examined, and (3) the model is modified and the process repeats. For examp...
Conference Paper
Full-text available
Multiaffine hybrid automata (MHA) represent a powerful formalism to model complex dynamical systems. This formalism is particularly suited for the representation of biological systems which often exhibit highly non-linear behavior. In this paper, we consider the problem of parameter identification for MHA. We present an abstraction of MHA based on...
Conference Paper
Full-text available
We present the first steps towards a runtime verification framework for monitoring hybrid and cyber-physical systems (CPS) development tools—such as hybrid systems reachability analysis tools, model-based development environments like Simulink/Stateflow (SLSF), etc.— based on randomized differential testing. First, hybrid automaton models are rando...
Conference Paper
Full-text available
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate represent...
Article
Full-text available
In this poster, we present methods for randomly generating hybrid automata with affine differential equations, invariants, guards, and assignments. Selecting an arbitrary affine function from the set of all affine functions results in a low likelihood of generating hybrid automata with diverse and interesting behaviors, as there are an uncountable...
Conference Paper
Ultimate Automizer is an automatic software verification tool for C programs. This tool is a prototype implementation of an automata-theoretic approach that allows a modular verification of programs. Furthermore, this is the first implementation of a novel interpolation technique where interpolants are not obtained from an interpolating theorem pro...
Conference Paper
A linear pseudo-Boolean constraint (LPB) [1,4,5] is an expression of the form a 1ℓ1 + … + a m ℓm ≥ d. Here each ℓi is a literal of the form x i or 1 –x i . An LPB can be used to represent a Boolean function; e.g. 2x 1 + x 2 + x 3 ≥ 2 represents the same function as the propositional formula x1 ∨ (x2 ∧ x3). Functions that can be represented by a sin...
Conference Paper
Ultimate Automizer is an automatic software verification tool for C programs. This tool is the first implementation of trace abstraction, which is an automata-theoretic approach to software verification. The implemented algorithm uses nested interpolants in its interprocedural program analysis. The interpolating SMT solver SMTInterpol is used to co...
Conference Paper
A linear pseudo-Boolean constraint (LPB) is an expres- sion of the form a1 ℓ1 +.+ am ℓm ≥ d, where each ℓi is a literal (it assumes the value 1 or 0 depending on whether a propositional variable xi is true or false) and a1;.. am; d are natural numbers. An LPB represents a Boolean function, and those Boolean functions that can be represented by exac...

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Project (1)
Project
Logical Methods in Computer Science (LogiCS) research project lies in the intersection of two broad areas: Databases and Artificial Intelligence, where logic is used to model, store, analyze and predict information about the outside world including the Internet. And the second one is Verification, where logic is used to model, analyze and construct computer programs themselves. The logical and algorithmic questions which underlie both application areas are studied in the area of Computational Logic.