Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data by Guoqiang Hu, Huanjie Li, Wei Zhao, Yuxing Hao, Zonglei Bai, Lisa D. Nickerson, Fengyu Cong

“…To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. …”
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Reconfigurable Analog Preprocessing for Efficient Asynchronous Analog-to-Digital Conversion by Brandon M. Kelly, Alexander T. DiLello, David W. Graham

“…The versatility and reprogrammability of this system allows a multitude of event-driven, asynchronous, or even purely data-driven quantization methods to be implemented for a variety of different applications. …”
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The relationship between common data-based indicators and the welfare of Swiss dairy herds by Barbara Lutz, Barbara Lutz, Sibylle Zwygart, Beat Thomann, Dimitri Stucki, Joan-Bryce Burla

“…Thus, the multidimensional welfare definition is insufficiently covered, and the present publication does not support the approach of a purely data-based prediction of dairy welfare status at the farm level. …”
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Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning by Anna Kovacs-Györi, Alina Ristea, Clemens Havas, Michael Mehaffy, Hartwig H. Hochmair, Bernd Resch, Levente Juhasz, Arthur Lehner, Laxmi Ramasubramanian, Thomas Blaschke

“…Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. …”
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EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates by Heid, Esther, Goldman, Samuel, Sankaranarayanan, Karthik, Coley, Connor Wilson, Flamm, Christoph, Green Jr, William H

“…Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. …”
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Robust assessment of railway vehicle safety risks in operation using a proposed data-driven wheel profile generation approach: Design of computer experiments and surrogate models by Braga, Joaquim A.P., Costa, João N., Ambrósio, Jorge, Frey, Daniel, Andrade, António R.

“…The applied methods consist of: (i) selection of predictors and pre-processing, based on literature, standards and a purely data-driven approach to generate wheel profiles; (ii) space-filling design, using Latin hypercube sampling; (iii) obtaining vehicle responses and post-processing, using a multibody dynamics commercial software and according to standards; (iv) surrogate modelling, using Gaussian processes and linear models; (v) sensitivity analysis, through Sobol indices; (vi) safety assessment, analysing response surfaces. …”
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Multifactorial evolutionary algorithm with online transfer parameter estimation : MFEA-II by Bali, Kavitesh Kumar, Ong, Yew-Soon, Gupta, Abhishek, Tan, Puay Siew

“…Our proposal is based on the principled theoretical arguments that seek to minimize the tendency of harmful interactions between tasks, based on a purely data-driven learning of relationships among them. …”
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Leveraging sanitized data for probabilistic electricity market prediction: a Singapore case study by Xu, Ning Zhou, Gao, Xiang, Chai, Songjian, Niu, Ming, Yang, Jia Xin

“…These insights are challenging to obtain using purely data-driven methods. This paper proposes a physics-based solution for the probabilistic prediction of market-clearing outcomes, using real sanitized offer data from the National Electricity Market of Singapore (NEMS). …”
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Finite difference-embedded UNet for solving transcranial ultrasound frequency-domain wavefield by Wang, Linfeng, Li, Jian, Chen, Shili, Fan, Zheng, Zeng, Zhoumo, Liu, Yang

“…Grid-based numerical solvers such as finite difference (FD) and finite element methods have limitations including high computational costs and discretization errors. Purely data-driven methods have relatively high demands on training datasets. …”
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Machine Learning for Model Error Inference and Correction by Massimo Bonavita, Patrick Laloyaux

“…In particular, we reconsider the fundamental constraints of a purely data‐driven approach to forecasting and provide a view on how to best integrate machine learning technologies within current data assimilation and forecasting methods.…”
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An integrative cancer classification based on gene expression data by Ong, H. F., Mustapha, Norwati

“…The advent of integrative approach has shifted cancer classification task from purely data-centric to incorporate prior biological knowledge. …”
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Deep generative models of LDLR protein structure to predict variant pathogenicity by Jose K. James, Kristjan Norland, Angad S. Johar, Iftikhar J. Kullo

“…ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. …”
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Mean Squared Variance Portfolio: A Mixed-Integer Linear Programming Formulation by Francisco Fernández-Navarro, Luisa Martínez-Nieto, Mariano Carbonero-Ruz, Teresa Montero-Romero

“…Additionally, a novel purely data-driven method for determining the optimal value of the hyper-parameter that is associated with the MV and MSV approaches is also proposed in this paper. …”
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Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework by Jifei Pan, Jingwei Xiong, Yihong Zhuo

“…Experiments show that the average recognition rate of the proposed model, with embedded radar knowledge, is improved by 33.7% compared with the purely data-driven model. Compared with other similar state-of-the-art reported models, such as AlexNet, VGGNet, LeNet, ResNet, and ConvNet, the recognition rate is increased by 12%. …”
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NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications by Tobias Thummerer, Johannes Stoljar, Lars Mikelsons

“…The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. …”
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Physics-Guided Residual Learning for Probabilistic Power Flow Analysis by Kejun Chen, Yu Zhang

“…Specifically, two model-based methods require the knowledge of system topology and line parameters, while the purely data-driven method can work without power grid parameters. …”
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Vehicular Applications of Koopman Operator Theory—A Survey by Waqas A. Manzoor, Samir Rawashdeh, Alireza Mohammadi

“…The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. …”
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Mapping secondary data gaps for social simulation modelling: A case study of Syrian asylum migration to Europe [version 1; peer review: 2 approved, 2 approved with reservations] by Jakub Bijak, Martin Hinsch, Sarah Nurse

“…By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.…”
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A PINN Surrogate Modeling Methodology for Steady-State Integrated Thermofluid Systems Modeling by Kristina Laugksch, Pieter Rousseau, Ryno Laubscher

“…Physics-informed neural networks (PINNs) were developed to overcome the limitations associated with the acquisition of large training data sets that are commonly encountered when using purely data-driven machine learning methods. This paper proposes a PINN surrogate modeling methodology for steady-state integrated thermofluid systems modeling based on the mass, energy, and momentum balance equations, combined with the relevant component characteristics and fluid property relationships. …”
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Mapping of Agricultural Crops from Single High-Resolution Multispectral Images—Data-Driven Smoothing vs. Parcel-Based Smoothing by Asli Ozdarici-Ok, Ali Ozgun Ok, Konrad Schindler

“…The main objective of this work is to expose performance difference between state-of-the-art parcel-based smoothing and purely data-driven conditional random field (CRF) smoothing, which is yet unknown. …”
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