Generative diffusion model surrogates for mechanistic agent-based biological models [PDF]
Machine Learning: Science and TechnologyMechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these
Tien Comlekoglu +5 more
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Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models [PDF]
Journal of Scientific Computing, 2022 Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency.
Sibo Cheng +7 more
semanticscholar +1 more source
Detecting Adversarial Examples Using Surrogate Models
Machine Learning and Knowledge Extraction, 2023 Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image ...
Borna Feldsar +2 more
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Machine learning based surrogate models for microchannel heat sink optimization [PDF]
Applied Thermal Engineering, 2022 Microchannel heat sinks are an efficient cooling method for semiconductor packages. However, to properly cool increasingly complex and thermally dense circuits, microchannel designs should be improved and expanded on.
Ante Sikirica +2 more
semanticscholar +1 more source
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation [PDF]
arXiv.org, 2022 The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
A. Adelmann +10 more
semanticscholar +1 more source
Mathematics, 2023 In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems.
Zongliang Guo +3 more
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Detecting and Isolating Adversarial Attacks Using Characteristics of the Surrogate Model Framework
Applied Sciences, 2023 The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the ...
Piotr Biczyk, Łukasz Wawrowski
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Eccentric binary black hole surrogate models for the gravitational waveform and remnant properties: Comparable mass, nonspinning case [PDF]
Physical Review D, 2021 We develop new strategies to build numerical relativity surrogate models for eccentric binary black hole systems, which are expected to play an increasingly important role in current and future gravitational-wave detectors.
Tousif Islam +8 more
semanticscholar +1 more source
Bayesian optimization with adaptive surrogate models for automated experimental design
npj Computational Materials, 2021 Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate.
Bowen Lei +6 more
semanticscholar +1 more source
Modelling for Digital Twins—Potential Role of Surrogate Models
Processes, 2021 The application of white box models in digital twins is often hindered by missing knowledge, uncertain information and computational difficulties. Our aim was to overview the difficulties and challenges regarding the modelling aspects of digital twin ...
Ágnes Bárkányi +3 more
semanticscholar +1 more source