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Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations

Kapoor, T. (author), Wang, H. (author), Nunez, Alfredo (author), Dollevoet, R.P.B.J. (author)

This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko beams on the Winkler foundation are simulated using a causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter...

journal article 2024

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Transfer learning-based methodologies for Dynamic Thermal Rating of transmission lines

Paldino, Gian Marco (author), De Caro, Fabrizio (author), De Stefani, J. (author), Vaccaro, Alfredo (author), Bontempi, Gianluca (author)

Dynamic Thermal Rating (DTR) enhances grid flexibility by adapting line capabilities to weather conditions. For this purpose, DTR-based technologies require reliable and continuous measurement of the conductor temperature along the line route, which could hinder their wide-scale deployment due to the prohibitively high number of required...

journal article 2024

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Computational models for clinical drug response prediction: aligning transcriptomic data of patients and pre-clinical models

Mourragui, S.M.C. (author)

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be...

doctoral thesis 2023

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Assessing the performances and transferability of graph neural network metamodels for water distribution systems

Kerimov, B. (author), Bentivoglio, Roberto (author), Garzón Díaz, J.A. (author), Isufi, E. (author), Tscheikner-Gratl, Franz (author), Steffelbauer, David Bernhard (author), Taormina, R. (author)

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based...

journal article 2023

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An in-depth analysis of passage-level label transfer for contextual document ranking

Rudra, Koustav (author), Fernando, Zeon Trevor (author), Anand, A. (author)

Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token...

journal article 2023

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Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis

Krcek, M. (author), Perin, G. (author)

Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different...

journal article 2023

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Cooperative data-driven modeling

Dekhovich, A. (author), Turan, O.T. (author), Jiaxiang, Y. (author), Bessa, M.A. (author)

Data-driven modeling in mechanics is evolving rapidly based on recent machine learning advances, especially on artificial neural networks. As the field matures, new data and models created by different groups become available, opening possibilities for cooperative modeling. However, artificial neural networks suffer from catastrophic...

journal article 2023

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Transfer learning for process design with reinforcement learning

Gao, Q. (author), Yang, Haoyu (author), Shanbhag, S.M. (author), Schweidtmann, A.M. (author)

Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process...

book chapter 2023

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Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems

Glynis, K.G. (author), Kapelan, Z. (author), Bakker, Martijn (author), Taormina, R. (author)

Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new...

journal article 2023

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Unsupervised Domain Adaptation for Disguised-gait-based Person Identification on Micro-Doppler Signatures

Yang, Yang (author), Yang, Xiaoyi (author), Sakamoto, Takuya (author), Fioranelli, F. (author), Li, Beichen (author), Lang, Yue (author)

In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably...

journal article 2022

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Convolutional neural network for predicting crack pattern and stress-crack width curve of air-void structure in 3D printed concrete

Chang, Z. (author), Wan, Z. (author), Xu, Y. (author), Schlangen, E. (author), Šavija, B. (author)

Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex microstructures that have high porosity and distinct anisotropy. Due to the material heterogeneity and rapid growth of cracks, fracture analysis in these air-void structures is often complex, resulting in a high computational cost. This study proposes a...

journal article 2022

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Assessment of Parkinson's Disease Severity from Videos Using Deep Architectures

Yin, Zhao (author), Geraedts, Victor Jacobus (author), Wang, Z. (author), Contarino, Maria Fiorella (author), Dibeklioglu, H. (author), van Gemert, J.C. (author)

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the...

journal article 2022

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Back to the Future: Solving Hidden Parameter MDPs with Hindsight

Ponnambalam, C.T. (author), Kamran, Danial (author), Simão, T. D. (author), Oliehoek, F.A. (author), Spaan, M.T.J. (author)

conference paper 2022

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Surrogate convolutional neural network models for steady computational fluid dynamics simulations

Eichinger, Matthias (author), Heinlein, A. (author), Klawonn, Axel (author)

A convolution neural network (CNN)-based approach for the construction of reduced order surrogate models for computational fluid dynamics (CFD) simulations is introduced; it is inspired by the approach of Guo, Li, and Iori [X. Guo, W. Li, and F. Iorio, Convolutional neural networks for steady flow approximation, in Proceedings of the 22nd ACM...

journal article 2022

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Dynamic transfer partial least squares for domain adaptive regression

Zhao, Zhijun (author), Yan, Gaowei (author), Ren, Mifeng (author), Cheng, Lan (author), Zhu, Zhujun (author), Pang, Y. (author)

The traditional soft sensor models are based on the independent and identical distribution assumption, which are difficult to adapt to changes in data distribution under multiple operating conditions, resulting in model performance deterioration. The domain adaptive transfer learning methods learn knowledge in different domains by means of...

journal article 2022

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Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures

Li, Xinyu (author), He, Yuan (author), Fioranelli, F. (author), Jing, Xiaojun (author)

Human activity recognition (HAR) plays a vital role in many applications, such as surveillance, in-home monitoring, and health care. Portable radar sensor has been increasingly used in HAR systems in combination with deep learning (DL). However, it is both difficult and time-consuming to obtain a large-scale radar dataset with reliable labels...

journal article 2022

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Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Zhang, Xinyu (author), Abbasi, Qammer H. (author), Fioranelli, F. (author), Romain, Olivier (author), Le Kernec, Julien (author)

Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler...

conference paper 2022

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Flowsheet Recognition using Deep Convolutional Neural Networks

Schulze Balhorn, L. (author), Gao, Q. (author), Goldstein, Dominik (author), Schweidtmann, A.M. (author)

Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through artificial intelligence. A large number of these flowsheets are publicly available in the scientific...

book chapter 2022

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On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets

Wei, Xiaolu (author), van der Zwaag, S. (author), Jia, Zixi (author), Wang, Chenchong (author), Xu, W. (author)

In this research a machine learning model for predicting the rotating bending fatigue strength and the high-throughput design of fatigue resistant steels is proposed. In this transfer prediction framework, machine learning models are first trained to estimate tensile properties (yield strength, tensile strength and elongation) on the basis of...

journal article 2022

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Increasing trust in complex machine learning systems: Studies in the music domain

Kim, Jaehun (author)

Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that...

doctoral thesis 2021