Publications related to Mathematical model

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our mod ...

WILEY2023

Plug-and-play adaptive surrogate modeling of parametric nonlinear dynamics in frequency domain

Jürg Alexander Schiffmann, Phillip Huwiler, Davide Pradovera

We present an algorithm for constructing efficient surrogate frequency-domain models of (nonlinear) parametric dynamical systems in a non-intrusive way. To capture the dependence of the underlying system on frequency and parameters, our proposed approach c ...

2023

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

Jan Sickmann Hesthaven, Niccolo' Discacciati

We propose a model order reduction technique to accurately approximate the behavior of multi-component systems without any a-priori knowledge of the coupled model. In the offline phase, we construct independent surrogate models by solving the local problem ...

WILEY2023

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

François Maréchal, Julia Granacher

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

Oxford2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Byzantine Machine Learning: A Primer

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot

The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

2023

Fast Adversarial Training With Adaptive Step Size

Sabine Süsstrunk, Mathieu Salzmann, Chen Liu, Zhuoyi Huang, Yong Zhang, Jue Wang

While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet. The key idea of recent works to ...

Piscataway2023

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Balanceable Coupled Piezoelectric NEMS Resonators

Johannes Mathis Valentin Lemonde

This project aims at designing, fabricating and characterising balanceable coupled NEMS resonators, in order to verify a theoretical model. In the field of sensing, coupled NEMS resonators benefit from an immensely increased sensitivity towards changes in ...

2022

A Comprehensive Timing Model for Accurate Frequency Tuning in Dataflow Circuits

Paolo Ienne, Andrea Guerrieri, Lana Josipovic, Carmine Rizzi

The ability of dataflow circuits to implement dynamic scheduling promises to overcome the conservatism of static scheduling techniques that high-level synthesis tools typically rely on. Yet, the same distributed control mechanism that allows dataflow circu ...

IEEE COMPUTER SOC2022

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A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Plug-and-play adaptive surrogate modeling of parametric nonlinear dynamics in frequency domain

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Byzantine Machine Learning: A Primer

Fast Adversarial Training With Adaptive Step Size

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Balanceable Coupled Piezoelectric NEMS Resonators

A Comprehensive Timing Model for Accurate Frequency Tuning in Dataflow Circuits

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

Plug-and-play adaptive surrogate modeling of parametric nonlinear dynamics in frequency domain

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Fast Adversarial Training With Adaptive Step Size

Byzantine Machine Learning: A Primer

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Balanceable Coupled Piezoelectric NEMS Resonators

A Comprehensive Timing Model for Accurate Frequency Tuning in Dataflow Circuits