{"total":72,"rows":[{"id":"2501.13329","title":"Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks","created":"2025-01-23","cited_by_count":20,"area":null},{"id":"2311.02225","title":"Multi-scale Time-stepping of Partial Differential Equations with Transformers","created":"2023-11-03","cited_by_count":14,"area":null},{"id":"2509.21670","title":"MORPH: PDE Foundation Models with Arbitrary Data Modality","created":"2025-09-25","cited_by_count":14,"area":null},{"id":"2405.20905","title":"VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification","created":"2024-05-31","cited_by_count":9,"area":null},{"id":"2310.16741","title":"Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets","created":"2023-10-25","cited_by_count":8,"area":null},{"id":"2403.13858","title":"A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators","created":"2024-03-19","cited_by_count":8,"area":null},{"id":"2305.15881","title":"Generative Adversarial Reduced Order Modelling","created":"2023-05-25","cited_by_count":7,"area":null},{"id":"2504.14264","title":"Generative emulation of chaotic dynamics with coherent prior","created":"2025-04-19","cited_by_count":7,"area":null},{"id":"2408.07847","title":"Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal","created":"2024-08-14","cited_by_count":6,"area":null},{"id":"2408.04969","title":"Towards aerodynamic surrogate modeling based on $\\beta$-variational autoencoders","created":"2024-08-09","cited_by_count":5,"area":null},{"id":"2402.05372","title":"Reduced-order modeling of unsteady fluid flow using neural network ensembles","created":"2024-02-08","cited_by_count":4,"area":null},{"id":"2406.03789","title":"Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction","created":"2024-06-06","cited_by_count":4,"area":null},{"id":"2601.05525","title":"Explainable AI: Learning from the Learners","created":"2026-01-09","cited_by_count":4,"area":null},{"id":"2605.05586","title":"AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling","created":"2026-05-07","cited_by_count":4,"area":null},{"id":"2307.06816","title":"Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder","created":"2023-07-10","cited_by_count":3,"area":null},{"id":"2308.02599","title":"Branched Latent Neural Maps","created":"2023-08-04","cited_by_count":3,"area":null},{"id":"2308.12874","title":"Easy attention: A simple attention mechanism for temporal predictions with transformers","created":"2023-08-24","cited_by_count":3,"area":null},{"id":"2312.11166","title":"Volume-Preserving Transformers for Learning Time Series Data with Structure","created":"2023-12-18","cited_by_count":3,"area":null},{"id":"2404.19660","title":"Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data","created":"2024-04-25","cited_by_count":3,"area":null},{"id":"2405.04161","title":"Decoding complexity: how machine learning is redefining scientific discovery","created":"2024-05-07","cited_by_count":3,"area":null},{"id":"2410.13376","title":"Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2412.05657","title":"Model-Agnostic AI Framework with Explicit Time Integration for Long-Term Fluid Dynamics Prediction","created":"2024-12-07","cited_by_count":3,"area":null},{"id":"2502.08683","title":"A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs","created":"2025-02-12","cited_by_count":3,"area":null},{"id":"2603.12635","title":"Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems","created":"2026-03-13","cited_by_count":3,"area":null},{"id":"2403.11591","title":"A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs","created":"2024-03-18","cited_by_count":2,"area":null},{"id":"2502.14679","title":"Disentangled Latent Spaces for Reduced Order Models using Deterministic Autoencoders","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2502.20132","title":"Regional climate projections using a deep-learning-based model-ranking and downscaling framework: Application to European climate zones","created":"2025-02-27","cited_by_count":2,"area":null},{"id":"2505.14595","title":"Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2508.04084","title":"Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows","created":"2025-08-06","cited_by_count":2,"area":null},{"id":"2510.25135","title":"Conditional neural field for spatial dimension reduction of turbulence data: a comparison study","created":"2025-10-29","cited_by_count":2,"area":null},{"id":"2312.02462","title":"Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2410.04193","title":"Parametric Taylor series based latent dynamics identification neural networks","created":"2024-10-05","cited_by_count":1,"area":null},{"id":"2410.18148","title":"Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction","created":"2024-10-23","cited_by_count":1,"area":null},{"id":"2502.04685","title":"Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2505.00473","title":"Spatially-Enhanced Temporal Fusion Transformer: Interpretable Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs","created":"2025-05-01","cited_by_count":1,"area":null},{"id":"2509.08515","title":"Variational Rank Reduction Autoencoders for Generative Thermal Design","created":"2025-09-10","cited_by_count":1,"area":null},{"id":"2511.00338","title":"A DeepONet joint Neural Tangent Kernel Hybrid Framework for Physics-Informed Inverse Source Problems and Robust Image Reconstruction","created":"2025-11-01","cited_by_count":1,"area":null},{"id":"2601.06183","title":"Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge","created":"2026-01-07","cited_by_count":1,"area":null},{"id":"2601.21681","title":"LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2610.01974","title":"Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems","created":"2026-10-01","cited_by_count":1,"area":null},{"id":"2405.10442","title":"Data-driven low-dimensional model of a sedimenting flexible fiber","created":"2024-05-16","cited_by_count":0,"area":null},{"id":"2503.23236","title":"UP-dROM : Uncertainty-Aware and Parametrised dynamic Reduced-Order Model, application to unsteady flows","created":"2025-03-29","cited_by_count":0,"area":null},{"id":"2505.15329","title":"Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows","created":"2025-05-21","cited_by_count":0,"area":null},{"id":"2507.21299","title":"Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics","created":"2025-07-21","cited_by_count":0,"area":null},{"id":"2511.21883","title":"Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2512.16305","title":"Can Transformers overcome the lack of data in the simulation of history-dependent flows?","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2512.19804","title":"Reduced Order Modeling for Tsunami Forecasting with Bayesian Hierarchical Pooling","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2601.11440","title":"GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance","created":"2026-01-16","cited_by_count":0,"area":null},{"id":"2601.19080","title":"Transformer Learning of Chaotic Collective Dynamics in Many-Body Systems","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2602.07765","title":"Disentangled Instrumental Variables for Causal Inference with Networked Observational Data","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2602.08478","title":"Time-Delayed Transformers for Data-Driven Modeling of Low-Dimensional Dynamics","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.23188","title":"Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2603.07525","title":"Generative prediction of laser-induced rocket ignition with dynamic latent space representations","created":"2026-03-08","cited_by_count":0,"area":null},{"id":"2603.17606","title":"End-to-end data-driven prediction of urban airflow and pollutant dispersion","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2603.18837","title":"Model Order Reduction of Cerebrovascular Hemodynamics Using POD_Galerkin and Reservoir Computing_based Approach","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2603.26023","title":"GLU: Global-Local-Uncertainty Fusion for Scalable Spatiotemporal Reconstruction and Forecasting","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2603.29224","title":"Derived Fields Preserve Fine-Scale Detail in Budgeted Neural Simulators","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.06475","title":"AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2604.11263","title":"Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2604.20372","title":"AI models of unstable flow exhibit hallucination","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.17888","title":"Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.21499","title":"Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2605.27756","title":"Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.05045","title":"Learning Control-Affine Reduced-Order Models via Autoencoders","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.07426","title":"Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2606.19562","title":"Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.23940","title":"Prediction of Viscoelastic Droplet Impact Dynamics Using a Vision Transformer-Based Approach","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.00669","title":"Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2607.24569","title":"The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2608.13827","title":"HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.21590","title":"Periodic Neural Mapping for Unsteady Rotor-Blade Pressure and Aeroelastic Load Prediction","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2610.15173","title":"Physics Foundation Models for Efficient Control of Fluid Flows","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}