Cover Image

Quantifying water-driven geometric uncertainties in powder bed concrete printing using high-resolution 3D modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wolf, Christoph, Hlaváček, Petr, Robens-Radermacher, Annika, Kadoke, Daniel, Unger, Jörg F.
Format:	Preprint
Published:	2026
Subjects:	Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing
by: Chiappetta, Mihaela, et al.
Published: (2023)

Spreading of highly cohesive metal powders with transverse oscillation kinematics
by: Weissbach, Reimar, et al.
Published: (2025)

Multivariate sensitivity-adaptive polynomial chaos expansion for high-dimensional surrogate modeling and uncertainty quantification
by: Loukrezis, Dimitrios, et al.
Published: (2023)

Quantifying model prediction sensitivity to model-form uncertainty
by: Portone, Teresa, et al.
Published: (2025)

Inference in the presence of model-form uncertainties: Leveraging a prediction-oriented approach to improve uncertainty characterization
by: White, Rebekah, et al.
Published: (2026)

A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
by: Linden, Lennart, et al.
Published: (2025)

Exploration of improved, roller-based spreading strategies for cohesive powders in additive manufacturing via coupled DEM-FEM simulations
by: Weissbach, Reimar, et al.
Published: (2023)

A variationally consistent beam-to-beam point coupling formulation for geometrically exact beam theories
by: Steinbrecher, Ivo, et al.
Published: (2026)

Mass conservation analysis of extrusion-based 3D printing simulations based on the level-set method
by: Rojas, Carlos J. G., et al.
Published: (2025)

Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams
by: Schommartz, Jasper O., et al.
Published: (2024)

Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration
by: Arcones, Daniel Andrés, et al.
Published: (2024)

Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields
by: Lian, Haojie, et al.
Published: (2024)

Accelerating model evaluations in uncertainty propagation on tensor grids using computational graph transformations
by: Wang, Bingran, et al.
Published: (2023)

Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design
by: Partovizadeh, Aylar, et al.
Published: (2024)

Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model
by: Cao, Zheng, et al.
Published: (2025)

Extension of graph-accelerated non-intrusive polynomial chaos to high-dimensional uncertainty quantification through the active subspace method
by: Wang, Bingran, et al.
Published: (2024)

Reduced-order modeling for second-order computational homogenization with applications to geometrically parameterized elastomeric metamaterials
by: Guo, T., et al.
Published: (2024)

Bayesian Updating of constitutive parameters under hybrid uncertainties with a novel surrogate model applied to biofilms
by: Fritsch, Lukas, et al.
Published: (2025)

Quantifying Bounded Rationality: Formal Verification of Simon's Satisficing Through Flexible Stochastic Dominance
by: Li, Jingyuan, et al.
Published: (2025)

VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
by: Conti, Paolo, et al.
Published: (2024)

Bayesian Tendon Breakage Localization under Model Uncertainty Using Distributed Fiber Optic Sensors
by: Arcones, Daniel Andrés, et al.
Published: (2026)

Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series
by: Žignić, Lucija, et al.
Published: (2024)

Topological and geometric characterization of synthetic aperture sonar collections
by: Robinson, Michael, et al.
Published: (2024)

Towards uncertainty quantification of a model for cancer-on-chip experiments
by: Bertoluzza, Silvia, et al.
Published: (2026)

Design of chemical recycling processes for PUR foam under uncertainty
by: Lotz, Patrick, et al.
Published: (2025)

Pore-scale modeling of capillary-driven binder migration during battery electrode drying
by: Weichel, Marcel, et al.
Published: (2026)

Computational design of personalized drugs via robust optimization under uncertainty
by: Altunay, Rabia, et al.
Published: (2025)

A gradient-enhanced univariate dimension reduction method for uncertainty propagation
by: Wang, Bingran, et al.
Published: (2024)

Bayesian estimation and uncertainty quantification of a temperature-dependent thermal conductivity
by: Silva, Rodrigo L. S., et al.
Published: (2024)

A consistent mixed-dimensional coupling approach for 1D Cosserat beams and 2D surfaces in 3D space
by: Steinbrecher, Ivo, et al.
Published: (2022)

Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning
by: Li, Krinos, et al.
Published: (2025)

A discontinuous Galerkin / cohesive zone model approach for the computational modeling of fracture in geometrically exact slender beams
by: Kota, Sai Kubair, et al.
Published: (2023)

Efficient damage simulations under material uncertainties in a weakly-intrusive implementation
by: Geisler, Hendrik, et al.
Published: (2024)

Data repairing and resolution enhancement using data-driven modal decomposition and deep learning
by: Hetherington, A., et al.
Published: (2024)

Parameter conditioned interpretable U-Net surrogate model for data-driven predictions of convection-diffusion-reaction processes
by: Kastor, Michael Urs Lars, et al.
Published: (2026)

Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
by: Wolf, Florian, et al.
Published: (2024)

A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems
by: Vlachas, Konstantinos, et al.
Published: (2024)

Adaptive coupling of peridynamic and classical continuum mechanical models driven by broken bond/strength criteria for structural dynamic failure
by: Li, JiuYi, et al.
Published: (2024)

Data-driven surrogate model for etch rate profiles using sensor data from a reactive ion etcher
by: Pamarty, Abhijit Pranav, et al.
Published: (2024)

Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches
by: Zlatić, Martin, et al.
Published: (2024)