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Found 6 papers, 3 papers with code
CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking
Our pre-trained model can improve the SOTAs in these tasks from 53% to 64%, 49% to 60%, and 74% to 94%, respectively.
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion
Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation.
A Survey on Time-Series Pre-Trained Models
Time-Series Mining (TSM) is an important research area since it shows great potential in practical applications.
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task.
PCNN: A physics-constrained neural network for multiphase flows
To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations.
A consistent and conservative model and its scheme for $N$-phase-$M$-component incompressible flows
Numerical tests indicate that the proposed model and scheme are effective and robust to study various challenging multiphase and multicomponent flows.
Computational Physics Numerical Analysis Numerical Analysis Fluid Dynamics
