arXiv Machine Learning
Jul 15
Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN
arXiv:2607. 12409v1 Announce Type: new Abstract: Parachutes are widely utilized in aviation, aerospace and lifesaving missions.
Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN
Parachutes are widely utilized in aviation, aerospace and lifesaving missions. As the initial stage of parachute deployment, suspension line extraction and straightening directly determines the smooth implementation of subsequent inflation procedures.
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arXiv Machine Learning
Jul 20
Trainable Spline Representations for Physics-Informed Learning
arXiv:2607. 15751v1 Announce Type: new Abstract: This work introduces Physics-Informed Splines (PI-Splines), a structured spline-based architecture for physics-informed learning.
arXiv Machine Learning
Jul 31
Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem
arXiv:2607. 27433v1 Announce Type: cross Abstract: We develop two parametric data-driven reduced models: a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order model (TROM), and apply both approaches to the parametrized one-dimensional shallow-water dam-break problem.
arXiv AI
Jun 6
Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data
arXiv:2606. 05199v1 Announce Type: cross Abstract: The identification of constitutive neural network models from heterogeneous full-field deformation data provides a robust alternative to traditional calibration methods based on homogeneous stress-strain experiments, particularly given the high dimensionality of trainable parameters.
arXiv Machine Learning
Jun 19
Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
arXiv:2606. 19375v1 Announce Type: new Abstract: Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial.
arXiv Machine Learning
Jul 8
SplineNet: An Isogeometric Deep Learning Method for Complex Shells
arXiv:2607. 06026v1 Announce Type: new Abstract: We present a novel isogeometric deep learning method, termed SplineNet, for the seamless design and analysis of shell structures with complex geometries.
arXiv AI
Jul 22
Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts
arXiv:2607. 19060v1 Announce Type: cross Abstract: Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks.
arXiv Machine Learning
Jul 1
Interface-Aware Neural Newton Preconditioning for Robust Cohesive Zone Model Simulations
arXiv:2606. 31921v1 Announce Type: new Abstract: Cohesive Zone Models (CZMs) are widely used to simulate interface fracture, delamination, adhesive failure, and fiber--matrix debonding in aerospace composite structures.
arXiv Machine Learning
Jun 3
Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
arXiv:2606. 03210v1 Announce Type: cross Abstract: Automatic-differentiation-based inverse analysis methods, including physics-informed neural networks (PINNs) and differentiable programming, have recently shown great promise due to their ability to compute accurate gradients and convergence efficiency.
arXiv Machine Learning
Jul 15
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
arXiv:2607. 12143v1 Announce Type: cross Abstract: Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activation.
arXiv Machine Learning
Jul 24
Automatic knot selection in smooth additive models
arXiv:2607. 21083v1 Announce Type: cross Abstract: B-spline regression constitutes a widely used framework for nonparametric modeling.
Hugging Face Trending Papers
Jun 3
Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
This study develops a two-domain physics-informed neural network framework for contaminant transport through a GCL/SL composite liner system, in which the thin GCL layer is treated using a steady-state advection-dispersion-biodegradation formulation and the underlying soil liner is modeled as a transient transport domain. Two formulations are evaluated against analytical and finite-element reference solutions under different leachate-head conditions: a standard PINN with soft constraint enforcement (Std-PINN) and a hard-constrained PINN (H-PINN), in which selected boundary and initial conditions are embedded directly into the trial solutions.