The 7th International Workshop on Symbolic-Numeric Methods for Reasoning about CPS and IoT


Welcome to the home page of the 7th International Workshop on Symbolic-Numeric Methods for Reasoning about CPS and IoT (SNR). SNR'21 will collocate with QONFEST'21 in Paris, France, on 23rd August 2021.

The workshop focuses on the combination of symbolic and numeric methods for reasoning about Cyber-Physical Systems and the Internet of Things to facilitate model identification, specification, verification, and control synthesis problems for these systems. The synergy between symbolic and numerical approaches is fruitful for two main reasons:

  • Symbolic methods that operate on exact and discrete representations of systems, the set of reachable states, the distribution of model parameters or the possible gains for controller parameters.

  • Numeric methods that operate on various forms of numerical approximations and continuous transformations of the systems, as developed in the area of continuous dynamical systems and control theory.

Such synergies are already seen in areas such as reachability analysis (symbolic representation of reachable states versus numerical integration), uncertainty reasoning (eg., Rao-Blackwellization), machine learning (eg., learning models through stochastic gradient descent versus symbolic reasoning over the function represented by the network to prove properties) and decision procedures (eg., symbolic SAT solvers versus numerical convex optimization solvers).

SNR'21 Program

We are happy to announce the program of the Workshop SNR’2021, which will be held on August 23 as part of QONFEST (

14:00 – 14:45 Stanley Bak (Invited talk)

Symbolic and Numeric Challenges in Neural Network Verification Methods

14:45 – 14:50 Questions

14:50 – 15:10 Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian noise (T. Badings, A. Abate, N. Jansen, D. Parker, H. Poonawala, M. Stoelinga)

15:10 – 15:30 Safety and Robustness Verification of Autoencoder based Regression Model using NNV Tool (N. Pal, T.T. Johnson)

15:45 – 16:30 Bardh Hoxha (Invited talk)

Autonomous CPS: Verification and Control

16:30 -16:35 Questions

16:35 – 16:55 Verification of Sigmoidal Artificial Neural Networks using iSAT

(D. Grundt, S. Liviu Jurj, W. Hagemann, P. Kröger, M. Fränzle)

17:00 -17:45 Nils Jansen (Invited talk)

Safe Planning under Epistemic Uncertainty and Partial Information

17:45 – 17:50 Questions

17:50 – 18:15 Model Checking for Rectangular Hybrid Systems: A Quantified Encoding Approach (W. Haddad, L. Nguyen, T.T. Johnson)

Time is Paris time (Central European Summertime).

Topics of Interest

The SNR workshop aims to catalyze work on the interface of symbolic and numeric methods for verification, synthesis and identification problems for CPS and IoT. The scope of the workshop includes, but is not restricted to, the following topics:

  • Verification, parameter identification and control synthesis for hybrid systems

  • Probabilistic inference and reachability for stochastic hybrid systems

  • Symbolic and numerical integration and decision techniques

  • Emerging applications to safe autonomous systems in uncertain environments

  • Resiliency and dependability in CPS and IoT

  • Temporal logic-based monitoring, reasoning and synthesis for CPS and IoT

We encourage submissions of papers in the following two specific areas:

  • Verification of models used in machine learning and autonomous CPS
    Learning algorithms are at the core of many engineering applications including robotics and autonomous vehicles. We invite research papers on verification of models used in machine learning and autonomous CPS. In particular, recent advances in autonomous cars require addressing challenging questions around their safety and reliability.

  • Symbolic and numerical techniques for verification and synthesis of stochastic models
    Autonomous systems operate in uncertain environments. Thus, it is essential to reason about the effect of uncertainty. We invite research papers on symbolic and numerical techniques for formal synthesis of stochastic systems.