The following pages link to Adam (Q34000):
Displaying 50 items.
- Residual networks as flows of diffeomorphisms (Q1988351) (← links)
- Denoising color images based on local orientation estimation and CNN classifier (Q1988467) (← links)
- Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (Q1989082) (← links)
- Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics (Q1989089) (← links)
- Study of the practical convergence of evolutionary algorithms for the optimal program control of a wheeled robot (Q1994806) (← links)
- A data-driven newsvendor problem: from data to decision (Q1999637) (← links)
- Novelty detection improves performance of reinforcement learners in fluctuating, partially observable environments (Q2001717) (← links)
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
- DGM: a deep learning algorithm for solving partial differential equations (Q2002333) (← links)
- Selection dynamics for deep neural networks (Q2003969) (← links)
- Towards easier and faster sequence labeling for natural language processing: a search-based probabilistic online learning framework (SAPO) (Q2004713) (← links)
- SP-Flow: self-supervised optical flow correspondence point prediction for real-time SLAM (Q2005168) (← links)
- Capturing deep tail risk via sequential learning of quantile dynamics (Q2007859) (← links)
- Parametric generation of conditional geological realizations using generative neural networks (Q2009823) (← links)
- Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks (Q2009855) (← links)
- Human body shape reconstruction from binary silhouette images (Q2010301) (← links)
- Preventing self-intersection with cycle regularization in neural networks for mesh reconstruction from a single RGB image (Q2010315) (← links)
- PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
- Computation of optimal transport and related hedging problems via penalization and neural networks (Q2020305) (← links)
- Multi-derivative physical and geometric convolutional embedding networks for skeleton-based action recognition (Q2020327) (← links)
- Parameterization for polynomial curve approximation via residual deep neural networks (Q2020358) (← links)
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering (Q2020738) (← links)
- A DNN-based data-driven modeling employing coarse sample data for real-time flexible multibody dynamics simulations (Q2020773) (← links)
- Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
- A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (Q2020834) (← links)
- Deep learning for model order reduction of multibody systems to minimal coordinates (Q2020838) (← links)
- A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model (Q2020945) (← links)
- Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics (Q2020980) (← links)
- Deep learned finite elements (Q2021024) (← links)
- A generic physics-informed neural network-based constitutive model for soft biological tissues (Q2021025) (← links)
- Towards blending physics-based numerical simulations and seismic databases using generative adversarial network (Q2021045) (← links)
- Modeling, simulation and machine learning for rapid process control of multiphase flowing foods (Q2021087) (← links)
- An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning (Q2021153) (← links)
- The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164) (← links)
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition (Q2021230) (← links)
- Data-driven learning of nonlocal physics from high-fidelity synthetic data (Q2021231) (← links)
- Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (Q2021252) (← links)
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
- Deep learning of thermodynamics-aware reduced-order models from data (Q2021918) (← links)
- Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
- Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (Q2021999) (← links)
- Universal machine learning for topology optimization (Q2022034) (← links)
- Non-invasive inference of thrombus material properties with physics-informed neural networks (Q2022055) (← links)
- A neural network-based framework for financial model calibration (Q2022121) (← links)
- Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (Q2022225) (← links)
- Neural networks-based backward scheme for fully nonlinear PDEs (Q2022970) (← links)
- Group level social media popularity prediction by MRGB and Adam optimization (Q2025074) (← links)
- A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretability (Q2026298) (← links)
- Geological facies modeling based on progressive growing of generative adversarial networks (GANs) (Q2027203) (← links)
- Subwords-only alternatives to fastText for morphologically rich languages (Q2027849) (← links)
