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Found 5 papers, 4 papers with code
DOF: Accelerating High-order Differential Operators with Forward Propagation
Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems.
Sleep When Everything Looks Fine: Self-Triggered Monitoring for Signal Temporal Logic Tasks
Existing works on online monitoring usually assume that the monitor can acquire system information periodically at each time instant.
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry.
Model Predictive Control for Signal Temporal Logic Specifications with Time Interval Decomposition
However, instead of applying MPC directly for the entire task horizon, we decompose the STL formula into several sub-formulae with disjoint time horizons, and shrinking horizon MPC is applied for each short-horizon sub-formula iteratively.
Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?
In particular, we leverage the concept of stability in the literature of partial differential equation to study the asymptotic behavior of the learned solution as the loss approaches zero.
