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Concept

Domain-driven design

Related publications (28)

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Olga Fink, Ismail Nejjar, Mengjie Zhao

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...

Elsevier2024

Chat2Code: A Chatbot for Model Specification and Code Generation, The Case of Smart Contracts

Shailesh Mishra

The potential of automatic code generation through Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to help software professionals write more accurate, reusable code, MDE frameworks could make programming accessible fo ...

Ieee Computer Soc2023

ACTOR: Action-Guided Kernel Fuzzing

Mathias Josef Payer

Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that ...

Usenix Assoc2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Roland Schwan, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

Bridging the gap between model-driven and data-driven methods in the era of Big Data

Gael Lederrey

Data-driven and model-driven methodologies can be regarded as competitive fields since they tackle similar problems such as prediction. However, these two fields can learn from each other to improve themselves. Indeed, data-driven methodologies have been d ...

EPFL2022

Contrastive Class-aware Adaptation for Domain Generalization

Mathieu Salzmann

Domain generalization (DG) tackles the problem of learning a model that generalizes to data drawn from a target domain that was unseen during training. A major trend in this area consists of learning a domain-invariant representation by minimizing the disc ...

IEEE2022

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Daniel Keller

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system work ...

2022

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Dimitrios Kyritsis, Jinzhi Lu

Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Dimitrios Kyritsis, Jinzhi Lu

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, deve ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Damage-driven strain localisation in networks of fibres: A computational homogenisation approach

Pablo Antolin Sanchez, Felipe Figueredo Rocha

In many applications, such as textiles, fibreglass, paper and several kinds of biological fibrous tissues, the main load-bearing constituents at the micro-scale are arranged as a fibre network. In these materials, rupture is usually driven by micro-mechani ...

2021

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Steffen Schneider

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

OASE 108. Ups & Downs. Reception Histories in Architecture

Christophe van Gerrewey

What is good, less good, or bad architecture? This issue of OASE examines how shifting appreciations, for very diverse reasons, can function as a productive misunderstanding, and as a lever to advance architectural criticism and pry thinking about architec ...

nai0102021

Heterotwin Zn3P2 superlattice nanowires: the role of indium insertion in the superlattice formation mechanism and their optical properties

Anna Fontcuberta i Morral, Jean-Baptiste Leran, Simon Robert Escobar Steinvall, Nicolas Tappy, Mahdi Zamani, Elias Zsolt Stutz, Rajrupa Paul, Reza Zamani, Lea Ghisalberti

Zinc phosphide (Zn3P2) nanowires constitute prospective building blocks for next generation solar cellsdue to the combination of suitable optoelectronic properties and an abundance of the constitutingelements in the Earth’s crust. The generation of periodi ...

2020

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

Blagovesta Hristova Kostova

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously ...

ASSOC COMPUTING MACHINERY2020

Materials Cloud, a platform for open computational science

Berend Smit, Nicola Marzari, Giovanni Pizzi, Christoph Leopold Talirz, Fernando Gargiulo, Martin Uhrin, Aliaksandr Yakutovich, Sebastiaan Philippe Huber, Snehal Pramod Kumbhar, Casper Welzel Andersen, Valeria Granata, Spyros Zoupanos, Elsa Passaro, Marco Borelli

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their ...

2020

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

Felix Schürmann, James Gonzalo King, Pramod Shivaji Kumbhar, Omar Awile, Jorge Blanco Alonso

Domain-specific languages (DSLs) play an increasingly important role in the generation of high performing software. They allow the user to exploit domain knowledge for the generation of more efficient code on target architectures. Here, we describe a new c ...

Springer2020

Embryonic lateral inhibition as optical modes: An analytical framework for mesoscopic pattern formation

Andrew Charles Oates, Jose Negrete Jr

Cellular checkerboard patterns are observed at many stages of embryonic development. We study an analytically tractable model for lateral inhibition and show that the steady states are analogous to optical phonons at the Gamma point, which have the wave nu ...

2019

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019