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Found 6 papers, 1 papers with code
Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab
We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible with Google Colab which allows automatic execution on cloud environment; (2) it supports two dimensional time-dependent PDEs; (3) it provides simple interface for definition of the residual loss, boundary condition and initial loss, together with their weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it allows for customizing the number of layers and neurons per layer, as well as for arbitrary activation function; (6) the learning rate and number of epochs are available as parameters; (7) it automatically differentiates PINN with respect to spatial and temporal variables; (8) it provides routines for plotting the convergence (with running average), initial conditions learnt, 2D and 3D snapshots from the simulation and movies (9) it includes a library of problems: (a) non-stationary heat transfer; (b) wave equation modeling a tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor growth simulations.
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING
Many problems such as node classification and link prediction in network data can be solved using graph embeddings, and a number of algorithms are known for constructing such embeddings.
Sonic: A Sampling-based Online Controller for Streaming Applications
It is challenging to find knob settings that optimize the run-time performance of such applications because the optimal knob settings are usually functions of inputs, computing platforms, time as well as user's requirements, which can be very diverse.
Optimizing Graph Transformer Networks with Graph-based Techniques
Unfortunately, the space overhead of this approach can be large, so in practice it is used only for small graphs.
Scalable Hypergraph Embedding System
While hypergraphs are a generalization of graphs, state-of-the-art graph embedding techniques are not adequate for solving prediction and classification tasks on large hypergraphs accurately in reasonable time.
An Elementary Introduction to Kalman Filtering
Kalman filtering is a classic state estimation technique used widely in engineering applications such as statistical signal processing and control of vehicles.
Systems and Control
