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README.md

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@@ -108,7 +108,7 @@ these sample files, users can gain an understanding of how to modify the dynamic
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reachability analysis. This feature helps users experiment with their analysis by using different settings to assess
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their effects on the overall computation of the reachable sets.-->
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For example, consider following dynamic system:
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For example, consider the following dynamic system:
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$$
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\begin{align*}
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| System | Code | Reachable Sets (Orange-NBA,Blue-BA) |
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|:-----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------:|
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| [synchronous machine](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pyrat/model/synchronous_machine.py) | [benchmark_synchronous_machine_cmp.py](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_synchronous_machine_cmp.py) | ![](doc/imgs/sync_machine_cmp.png) |
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| [Lotka Volterra model of 2 variables](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pyrat/model/lotka_volterra_2d.py) | [benchmark_lotka_volterra_2d_cmp.py](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_lotka_volterra_2d_cmp.py) | ![](doc/imgs/lotka_volterra_2d_cmp.png) |
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| [Jet engine](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pyrat/model/jet_engine.py) | [benchmark_jet_engine_cmp.py](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_jet_engine_cmp.py) | ![](doc/imgs/jet_engine_cmp.png) |
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| [synchronous machine](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pybdr/model/synchronous_machine.py) | [benchmark_synchronous_machine_cmp.py](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_synchronous_machine_cmp.py) | ![](doc/imgs/sync_machine_cmp.png) |
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| [Lotka Volterra model of 2 variables](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pybdr/model/lotka_volterra_2d.py) | [benchmark_lotka_volterra_2d_cmp.py](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_lotka_volterra_2d_cmp.py) | ![](doc/imgs/lotka_volterra_2d_cmp.png) |
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| [Jet engine](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pybdr/model/jet_engine.py) | [benchmark_jet_engine_cmp.py](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_jet_engine_cmp.py) | ![](doc/imgs/jet_engine_cmp.png) |
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For large time horizons, i.e. consider
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the system [Brusselator](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pyrat/model/brusselator.py)
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the system [Brusselator](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pybdr/model/brusselator.py)
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> For more details about the following example, please refer to
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> our [code](https://github.com/ASAG-ISCAS/PyBDR/blob/master/benchmarks/benchmark_brusselator_cmp.py).
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@@ -181,7 +181,8 @@ the system [Brusselator](https://github.com/ASAG-ISCAS/PyBDR/blob/master/pyrat/m
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## Computing Reachable Sets based on Boundary Analysis for Neural ODE
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For example, consider a neural ODE with following parameters and $\textit{sigmoid}$ activation function, also evaluated
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For example, consider a neural ODE with the following parameters and $\textit{sigmoid}$ activation function, also
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evaluated
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in <a href="https://link.springer.com/content/pdf/10.1007/978-3-031-15839-1_15.pdf"><strong>'Manzanas Lopez, D., Musau,
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P., Hamilton, N. P., & Johnson, T. T. Reachability analysis of a general class of neural ordinary differential
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equations. In Formal Modeling and Analysis of Timed Systems: 20th International Conference, FORMATS 2022, Warsaw,

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