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Found 18 papers, 5 papers with code
Physics-informed reduced order model with conditional neural fields
This study presents the conditional neural fields for reduced-order modeling (CNF-ROM) framework to approximate solutions of parametrized partial differential equations (PDEs).
Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion.
Quantifying Qualitative Insights: Leveraging LLMs to Market Predict
However, challenges such as insufficient context when fusing multimodal information and the difficulty in measuring the utility of qualitative outputs, which LLMs generate as text, have limited their effectiveness in tasks such as financial forecasting.
Harnessing On-Machine Metrology Data for Prints with a Surrogate Model for Laser Powder Directed Energy Deposition
In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process.
Physics-informed active learning with simultaneous weak-form latent space dynamics identification
The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems.
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems.
tLaSDI: Thermodynamics-informed latent space dynamics identification
We propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics.
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations
Traditional partial differential equation (PDE) solvers can be computationally expensive, which motivates the development of faster methods, such as reduced-order-models (ROMs).
Weak-Form Latent Space Dynamics Identification
With WLaSDI, the local latent space is obtained using weak-form equation learning techniques.
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer
Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of information.
GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder.
Certified data-driven physics-informed greedy auto-encoder simulator
A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-encoder simulators of high-dimensional nonlinear dynamical systems.
Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-meshkov Instabilities
Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface.
gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification
To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly.
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds.
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs).
Efficient nonlinear manifold reduced order model
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i. e., the solution space has a small Kolmogorov n-width.
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
A speedup of up to 2. 6 for 1D Burgers' and a speedup of 11. 7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.
