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imaging. Although it opens the way to faster data acquisition in industry and less
dangerous scans in medicine, standard approaches, such as the filtered backprojection
(FBP) algorithm or the widely used total-variation functional, often produce various
artefacts that hinder the diagnosis. With the rise of deep learning, many modern
techniques have proven themselves successful in removing such artefacts but at the cost of
large datasets. In this paper, we propose a new model-driven approach based on the
method of the approximate inverse, which could serve as new starting point for learning
strategies in the future. In contrast to FBP-type approaches, our reconstruction step
consists in evaluating linear functionals on the measured data using reconstruction kernels
that are precomputed as solution of an auxiliary problem. With this problem being
uniquely solvable, the derived limited-angle reconstruction kernel (LARK) is able to fully
reconstruct the object without the well-known streak artefacts, even for large limited
angles. However, it inherits severe ill-conditioning which leads to a different kind of
artefacts arising from the singular functions of the limited-angle Radon transform. The
problem becomes particularly challenging when working on semi-discrete (real or
analytical) measurements. We develop a general regularization strategy by combining
spectral filter, the method of the approximate inverse and custom edge-preserving
denoising in order to stabilize the whole process. We further derive and interpret error
estimates for the application on real, i.e. semi-discrete, data and we validate our approach
on synthetic and real data.", "doi" : "10.1088/1361-6420/ae5844", "bibtexKey": "Hahn_2026" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cd0d95c2801c2ffdc85ff3a211a3af2d/simtechpuma", "tags" : [ "PN3","PN6","EXC2075","selected" ], "intraHash" : "cd0d95c2801c2ffdc85ff3a211a3af2d", "interHash" : "f693360d2d45e3f9b26f5cfd569849da", "label" : "Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials", "user" : "simtechpuma", "description" : "", "date" : "2026-04-13 14:54:39", "changeDate" : "2026-04-13 14:54:39", "count" : 5, "pub-type": "article", "journal": "J. Chem. Theory Comput.", "year": "2026", "url": "http://dx.doi.org/10.1021/acs.jctc.5c01767", "author": [ "Moritz R. 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We identify the maximum model width that supports feature learning, which, in our setups, we find to be more than ten times smaller than typical large language model widths.", "doi" : "10.1088/2632-2153/ae4442", "bibtexKey": "Nikolaou_2026" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e0b01cb7a9056480740c346814c8e3f7/simtech", "tags" : [ "curated","PN6","PN6-5(II)","EXC2075" ], "intraHash" : "e0b01cb7a9056480740c346814c8e3f7", "interHash" : "3d9e0fd219e61bbd8c4713622fd0680e", "label" : "Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion\u2013Sorption Models", "user" : "simtech", "description" : "", "date" : "2026-02-19 11:39:25", "changeDate" : "2026-03-24 13:59:36", "count" : 6, "pub-type": "article", "journal": "Frontiers in Water: Advances in Model-Data Fusion for Water Resources Problems", "year": "2026 (submitted)", "url": "", "author": [ "Stefania Scheurer","Riccardo Frenner","Tim Brünnette","Sergey Oladyshkin","Wolfgang Nowak" ], "authors": [ {"first" : "Stefania", "last" : "Scheurer"}, {"first" : "Riccardo", "last" : "Frenner"}, {"first" : "Tim", "last" : "Brünnette"}, {"first" : "Sergey", "last" : "Oladyshkin"}, {"first" : "Wolfgang", "last" : "Nowak"} ], "bibtexKey": "scheurer2026confidence" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2220c6d85e156e27f5708a9bb8bd43703/simtechpuma", "tags" : [ "unclear","PN6-8(II)","PN6","EXC2075" ], "intraHash" : "220c6d85e156e27f5708a9bb8bd43703", "interHash" : "de2965a166288b23d3e079c4ea3ff25d", "label" : "Voronoi Cell Interface\u2010Based Parameter Sensitivity Analysis for Labeled Samples", "user" : "simtechpuma", "description" : "", "date" : "2026-02-16 15:04:58", "changeDate" : "2026-02-16 15:04:58", "count" : 7, "pub-type": "article", "journal": "Computer Graphics Forum","publisher":"Wiley", "year": "2025", "url": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.70122", "author": [ "Ruben Bauer","Marina Evers","Quynh Quang Ngo","Guido Reina","Steffen Frey","Michael Sedlmair" ], "authors": [ {"first" : "Ruben", "last" : "Bauer"}, {"first" : "Marina", "last" : "Evers"}, {"first" : "Quynh Quang", "last" : "Ngo"}, {"first" : "Guido", "last" : "Reina"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Michael", "last" : "Sedlmair"} ], "affiliation" : "Bauer, Ruben, Visualisierungsinstitut der Universität Stuttgart. 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Crespo Delgadillo","M. Kraft","Anneli Guthke" ], "authors": [ {"first" : "Thomas", "last" : "Wöhling"}, {"first" : "A.O.", "last" : "Crespo Delgadillo"}, {"first" : "M.", "last" : "Kraft"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "63","pages": "484-505", "doi" : "10.1111/gwat.13487", "bibtexKey": "wohling2024comparing" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/25f15d6ba062746dd8a082bc99164222d/simtechpuma", "tags" : [ "PN6","PN6A-4","EXC2075","selected" ], "intraHash" : "5f15d6ba062746dd8a082bc99164222d", "interHash" : "ccd8dd1775381685c4bffd04e3d0485b", "label" : "When Physics Gets in the Way: An Entropy-based Evaluation of Conceptual Constraints in Hybrid Hydrological Models", "user" : "simtechpuma", "description" : "", "date" : "2026-02-09 16:44:25", "changeDate" : "2026-02-09 16:44:25", "count" : 1, "pub-type": "article", "journal": "Hydrology and Earth System Sciences", "year": "2025", "url": "", "author": [ "Manuel Álvarez Chaves","Eduardo Acuña Espinoza","Uwe Ehret","Anneli Guthke" ], "authors": [ {"first" : "Manuel", "last" : "Álvarez Chaves"}, {"first" : "Eduardo", "last" : "Acuña Espinoza"}, {"first" : "Uwe", "last" : "Ehret"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "accepted", "bibtexKey": "alvarezchaves2025physics" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/247d8d16c0daf02723d0d9adf3a44e9ff/simtechpuma", "tags" : [ "PN6","misc","EXC2075","PN6-14" ], "intraHash" : "47d8d16c0daf02723d0d9adf3a44e9ff", "interHash" : "76f3c992f69111d33ec521b575931aac", "label" : "Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection", "user" : "simtechpuma", "description" : "", "date" : "2026-02-09 16:44:25", "changeDate" : "2026-02-09 16:44:25", "count" : 4, "pub-type": "proceedings", "publisher":"BMVC conference, MPI workshop", "year": "2025", "url": "https://bmva-archive.org.uk/bmvc/2025/assets/workshops/MPI/Paper_1/paper.pdf", "author": [ "Alina Roitberg Sarvenaz Babakhani, David Remy" ], "authors": [ {"first" : "Alina Roitberg", "last" : "Sarvenaz Babakhani, David Remy"} ], "abstract": "Energy expenditure estimation aims to infer human metabolic rate from physiological signals such as heart rate, respiration, or accelerometer, and has been studied primarily with classical regression methods. The few existing deep learning approaches rarely disentangle the role of neural architecture from that of signal choice. In this work, we systematically evaluate both aspects. We compare classical baselines with newer neural architectures across single signals, signal pairs, and grouped sensor inputs for diverse physical activities. Our results show that minute ventilation is the most predictive individual signal, with a transformer model achieving the lowest root mean square error (RMSE) of 0.87W/kg across all activities. Paired and grouped signals, such as those from the Hexoskin smart shirt (5 signals), offer good alternatives for faster models like CNN and ResNet with attention. Per-activity evaluation revealed mixed outcomes: notably better outcomes in low-intensity activities (RMSE down to 0.29W/kg; NRMSE = 0.04), while higher-intensity tasks showed larger RMSE but more comparable normalized errors. Finally, subject-level analysis highlights strong inter-individual variability, motivating the need for adaptive modeling strategies. Our code and models will be publicly available at this GitHub repository: https://github.com/Sarvibabakhani/deeplearning-biosignals-ee", "language" : "english", "bibtexKey": "noauthororeditor" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/20d0a260ffdf08f7d114fc508b8e2b33f/simtechpuma", "tags" : [ "PN3-11","PN4","PN3","PN6","EXC2075","selected" ], "intraHash" : "0d0a260ffdf08f7d114fc508b8e2b33f", "interHash" : "0b1970f7c28aa561666ea785dfb94a98", "label" : "ART-SM: Boosting Fragment-Based Backmapping by Machine Learning", "user" : "simtechpuma", "description" : "", "date" : "2026-02-09 16:44:25", "changeDate" : "2026-02-09 16:44:25", "count" : 5, "pub-type": "article", "journal": "Journal of Chemical Theory and Computation","publisher":"American Chemical Society (ACS)", "year": "2025", "url": "http://dx.doi.org/10.1021/acs.jctc.5c00189", "author": [ "Christian Pfaendner","Viktoria Korn","Pritom Gogoi","Benjamin Unger","Kristyna Pluhackova" ], "authors": [ {"first" : "Christian", "last" : "Pfaendner"}, {"first" : "Viktoria", "last" : "Korn"}, {"first" : "Pritom", "last" : "Gogoi"}, {"first" : "Benjamin", "last" : "Unger"}, {"first" : "Kristyna", "last" : "Pluhackova"} ], "volume": "21","number": "8","pages": "4151\u20134166", "issn" : "1549-9626", "doi" : "10.1021/acs.jctc.5c00189", "bibtexKey": "Pfaendner_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e90294c3b5b554eaf29e779847e41793/simtech", "tags" : [ "curated","PN6","PN6A-4","EXC2075" ], "intraHash" : "e90294c3b5b554eaf29e779847e41793", "interHash" : "3fcd475e81478032c4de1c5732f0589f", "label" : "When Physics Gets in the Way: An Entropy-based Evaluation of Conceptual Constraints in Hybrid Hydrological Models", "user" : "simtech", "description" : "", "date" : "2026-02-04 09:17:34", "changeDate" : "2026-02-04 09:17:34", "count" : 5, "pub-type": "article", "journal": "Hydrology and Earth System Sciences", "year": "2026", "url": "", "author": [ "Manuel Álvarez Chaves","Eduardo Acuña Espinoza","Uwe Ehret","Anneli Guthke" ], "authors": [ {"first" : "Manuel", "last" : "Álvarez Chaves"}, {"first" : "Eduardo", "last" : "Acuña Espinoza"}, {"first" : "Uwe", "last" : "Ehret"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "30","number": "3","pages": "629--658", "doi" : "10.5194/hess-30-629-2026", "bibtexKey": "alvarezchaves2025physics" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/204ce87872250fefd91fcf4cdfd8965b9/simtech", "tags" : [ "curated","PN6","PN6A-4","EXC2075" ], "intraHash" : "04ce87872250fefd91fcf4cdfd8965b9", "interHash" : "5e72757dfa96fce2fd2a993c390212d3", "label" : "A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling", "user" : "simtech", "description" : "", "date" : "2026-01-20 12:22:07", "changeDate" : "2026-01-20 12:22:07", "count" : 5, "pub-type": "article", "journal": "Machine Learning: Earth", "year": "2026", "url": "", "author": [ "Manuel Álvarez Chaves","Eduardo Acuña Espinoza","Daniel Klotz","Hoshin Gupta","Uwe Ehret","Anneli Guthke" ], "authors": [ {"first" : "Manuel", "last" : "Álvarez Chaves"}, {"first" : "Eduardo", "last" : "Acuña Espinoza"}, {"first" : "Daniel", "last" : "Klotz"}, {"first" : "Hoshin", "last" : "Gupta"}, {"first" : "Uwe", "last" : "Ehret"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "under review", "bibtexKey": "alvarezchaves2026variational" } ] }