Cover Image

Physics-Informed Diffusion Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bastek, Jan-Hendrik, Sun, WaiChing, Kochmann, Dennis M.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Informed Neural Networks for Shell Structures
by: Bastek, Jan-Hendrik, et al.
Published: (2022)

Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models
by: Bastek, Jan-Hendrik, et al.
Published: (2023)

Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
by: Bahmani, Bahador, et al.
Published: (2023)

Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials
by: Bahmani, Bahador, et al.
Published: (2023)

Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
by: Kalina, Karl A., et al.
Published: (2024)

Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables
by: Rosenkranz, Max, et al.
Published: (2024)

PIVONet: A Physically-Informed Variational Neuro ODE Model for Efficient Advection-Diffusion Fluid Simulation
by: Cheung, Hei Shing, et al.
Published: (2026)

Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers
by: Zheng, Li, et al.
Published: (2025)

Physically Informed Synchronic-adaptive Learning for Industrial Systems Modeling in Heterogeneous Media with Unavailable Time-varying Interface
by: Wang, Aina, et al.
Published: (2024)

HyperCAN: Hypernetwork-Driven Deep Parameterized Constitutive Models for Metamaterials
by: Zheng, Li, et al.
Published: (2024)

A review on data-driven constitutive laws for solids
by: Fuhg, Jan Niklas, et al.
Published: (2024)

Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
by: Wang, Zihao, et al.
Published: (2024)

Physics-Informed Deep Learning Model for Line-integral Diagnostics Across Fusion Devices
by: Wang, Cong, et al.
Published: (2024)

Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
by: Khan, Shahroz, et al.
Published: (2024)

Physics-Informed Machine Learning for Smart Additive Manufacturing
by: Sharma, Rahul, et al.
Published: (2024)

Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
by: Nasiri, Mahdi, et al.
Published: (2024)

Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
by: Sarker, Apurba
Published: (2024)

PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
by: Zhao, Zhiyuan, et al.
Published: (2023)

A mixed-order quasicontinuum approach for beam-based architected materials with application to fracture
by: Kraschewski, Kevin, et al.
Published: (2024)

Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator
by: Oddiraju, Manaswin, et al.
Published: (2024)

Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model
by: Etienam, Clement, et al.
Published: (2024)

Physics-Informed Learning of Flow Distribution and Receiver Heat Losses in Parabolic Trough Solar Fields
by: Matthes, Stefan, et al.
Published: (2025)

Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks
by: Shah, Naval
Published: (2025)

PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models
by: Yuan, Mingze, et al.
Published: (2025)

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
by: Zhang, Yi, et al.
Published: (2025)

Generative Aerodynamic Design with Diffusion Probabilistic Models
by: Wagenaar, Thomas, et al.
Published: (2024)

Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
by: Sun, Haoran, et al.
Published: (2025)

Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling
by: Zheng, Li, et al.
Published: (2023)

The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithms
by: Thoi, Trang, et al.
Published: (2026)

Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
by: Choudhary, Himanshu, et al.
Published: (2025)

Feature Mapping in Physics-Informed Neural Networks (PINNs)
by: Zeng, Chengxi, et al.
Published: (2024)

Transferable Physics-Informed Representations via Closed-Form Head Adaptation
by: Wong, Jian Cheng, et al.
Published: (2026)

StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction
by: Wang, Shengkun, et al.
Published: (2024)

EqOD: Symmetry-Informed Stability Selection for PDE Identification
by: N'guessan, Gnankan Landry Regis, et al.
Published: (2026)

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
by: Logakannan, Krishna Prasath, et al.
Published: (2025)

GenCP: Towards Generative Modeling Paradigm of Coupled Physics
by: Gao, Tianrun, et al.
Published: (2026)

Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
by: Perini, Janne, et al.
Published: (2026)

Non-Invasive Glucose Prediction System Enhanced by Mixed Linear Models and Meta-Forests for Domain Generalization
by: Sun, Yuyang, et al.
Published: (2024)

DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models
by: Daiya, Divyanshu, et al.
Published: (2024)

A Reward-Directed Diffusion Framework for Generative Design Optimization
by: Keramati, Hadi, et al.
Published: (2025)