A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Noor, Toiba, Lone, Soban Nasir, Ramana, G. V., Nayek, Rajdip
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification
by: Lone, Soban Nasir, et al.
Published: (2024)

Probabilistic Digital Twin for Misspecified Structural Dynamical Systems via Latent Force Modeling and Bayesian Neural Networks
by: Kashyap, Sahil, et al.
Published: (2025)

Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction
by: Thorat, Rohan Vitthal, et al.
Published: (2026)

Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
by: Kumar, Sawan, et al.
Published: (2024)

Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
by: Kumar, Sawan, et al.
Published: (2024)

Safe Reinforcement Learning-Based Vibration Control: Overcoming Training Risks with LQR Guidance
by: Thorat, Rohan Vitthal, et al.
Published: (2025)

From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
by: Kumar, Sawan, et al.
Published: (2025)

Symmetry-enforcing neural networks with applications to constitutive modeling
by: Garanger, Kévin, et al.
Published: (2023)

Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials
by: Tacke, Marius, et al.
Published: (2025)

Smoothness and monotonicity constraints for neural networks using ICEnet
by: Richman, Ronald, et al.
Published: (2023)

Computing frustration and near-monotonicity in deep neural networks
by: Wendin, Joel, et al.
Published: (2025)

Atrial constitutive neural networks
by: Peirlinck, Mathias, et al.
Published: (2025)

Size and depth of monotone neural networks: interpolation and approximation
by: Mikulincer, Dan, et al.
Published: (2022)

An alternative approach to train neural networks using monotone variational inequality
by: Xu, Chen, et al.
Published: (2022)

Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
by: Anton, David, et al.
Published: (2024)

Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
by: McCulloch, Jeremy A., et al.
Published: (2025)

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
by: Hlaing, N., et al.
Published: (2022)

Bayesian Supervised Causal Clustering
by: Wang, Luwei, et al.
Published: (2026)

Bayesian continual learning and forgetting in neural networks
by: Bonnet, Djohan, et al.
Published: (2025)

Bayesian sparsification for deep neural networks with Bayesian model reduction
by: Marković, Dimitrije, et al.
Published: (2023)

Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated study
by: Myklebust, Even Moa, et al.
Published: (2024)

On Feynman--Kac training of partial Bayesian neural networks
by: Zhao, Zheng, et al.
Published: (2023)

Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)
by: Thakolkaran, Prakash, et al.
Published: (2025)

Analog Bayesian neural networks are insensitive to the shape of the weight distribution
by: Patel, Ravi G., et al.
Published: (2025)

Large deviation principles for convolutional Bayesian neural networks
by: Bassetti, Federico, et al.
Published: (2026)

Bayesian neural networks with interpretable priors from Mercer kernels
by: Alberts, Alex, et al.
Published: (2025)

Physics Informed Neural Networks for Free Shear Flows
by: Raghu, Siddharth, et al.
Published: (2024)

Integrating Bayesian methods with neural network--based model predictive control: a review
by: Karacelik, Asli
Published: (2025)

Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights
by: Kong, Insung, et al.
Published: (2024)

An axiomatized PDE model of deep neural networks
by: Wang, Tangjun, et al.
Published: (2023)

Student-t processes as infinite-width limits of posterior Bayesian neural networks
by: Caporali, Francesco, et al.
Published: (2025)

Improved uncertainty quantification for neural networks with Bayesian last layer
by: Fiedler, Felix, et al.
Published: (2023)

Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
by: Jantre, Sanket, et al.
Published: (2023)

FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
by: Kumbhakar, Prakash, et al.
Published: (2026)

Incorporating graph neural network into route choice model
by: Ma, Yuxun, et al.
Published: (2025)

Uncertainty propagation in feed-forward neural network models
by: Diamzon, Jeremy, et al.
Published: (2025)

The recursive scheme of clustering
by: Miniak-Górecka, Alicja, et al.
Published: (2024)

Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models
by: Tan, Pengcheng, et al.
Published: (2026)

Fixed points of nonnegative neural networks
by: Piotrowski, Tomasz J., et al.
Published: (2021)

Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies
by: Allier, Cédric, et al.
Published: (2026)