4 research outputs found
Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps
The ability for groundwater heat pumps to meet space heating and cooling
demands without relying on fossil fuels, has prompted their mass roll out in
dense urban environments. In regions with high subsurface groundwater flow
rates, the thermal plume generated from a heat pump's injection well can
propagate downstream, affecting surrounding users and reducing their heat pump
efficiency. To reduce the probability of interference, regulators often rely on
simple analytical models or high fidelity groundwater simulations to determine
the impact that a heat pump has on the subsurface aquifer and surrounding heat
pumps. These are either too inaccurate or too computationally expensive for
everyday use. In this work, a surrogate model was developed to provide a quick,
high accuracy prediction tool of the thermal plume generated by a heat pump
within heterogeneous subsurface aquifers. Three variations of a convolutional
neural network were developed that accepts the known groundwater Darcy
velocities as discrete two-dimensional inputs and predicts the temperature
within the subsurface aquifer around the heat pump. A data set consisting of
800 numerical simulation samples, generated from random permeability fields and
pressure boundary conditions, was used to provide pseudo-randomized Darcy
velocity fields as input fields and the temperature field solution for training
the network. The subsurface temperature field output from the network provides
a more realistic temperature field that follows the Darcy velocity streamlines,
while being orders of magnitude faster than conventional high fidelity solversComment: 24 pages, 11 figure
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Machine learning-based modeling of physical systems has experienced increased
interest in recent years. Despite some impressive progress, there is still a
lack of benchmarks for Scientific ML that are easy to use but still challenging
and representative of a wide range of problems. We introduce PDEBench, a
benchmark suite of time-dependent simulation tasks based on Partial
Differential Equations (PDEs). PDEBench comprises both code and data to
benchmark the performance of novel machine learning models against both
classical numerical simulations and machine learning baselines. Our proposed
set of benchmark problems contribute the following unique features: (1) A much
wider range of PDEs compared to existing benchmarks, ranging from relatively
common examples to more realistic and difficult problems; (2) much larger
ready-to-use datasets compared to prior work, comprising multiple simulation
runs across a larger number of initial and boundary conditions and PDE
parameters; (3) more extensible source codes with user-friendly APIs for data
generation and baseline results with popular machine learning models (FNO,
U-Net, PINN, Gradient-Based Inverse Method). PDEBench allows researchers to
extend the benchmark freely for their own purposes using a standardized API and
to compare the performance of new models to existing baseline methods. We also
propose new evaluation metrics with the aim to provide a more holistic
understanding of learning methods in the context of Scientific ML. With those
metrics we identify tasks which are challenging for recent ML methods and
propose these tasks as future challenges for the community. The code is
available at https://github.com/pdebench/PDEBench.Comment: 16 pages (main body) + 34 pages (supplemental material), accepted for
publication in NeurIPS 2022 Track Datasets and Benchmark
Telekommunikationsüberwachung
Referat über die Überwachungsgrundlagen in der BRD im Seminar Rechtsprobleme des E-Commerce bei Prof. Dr. Hansjürgen Garstka (WS 2002/02) an der TU Berli