TerraPINN · Toward fully physics based probabilistic seismic hazard assessment using physics informed neural networks
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2022-03-01 → 2024-02-29
- Финансиране от ЕС
- 224 934 €
- Участници
- 1
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Невронните мрежи, базирани на физични закони, се изследват за по-бързо изчисляване на разпространението на сеизмичните вълни. Това помага за по-доброто разбиране на земетресенията и рисковете им за човешкия живот и инфраструктурата.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Toward fully physics based probabilistic seismic hazard assessment using physics informed neural networks
The ability to accurately simulate seismic wavefields is an essential part of our ability to understand seismic hazard and it's potential risks to life and built infrastructure. It is also a key component in our ability to image Earth's subsurface, which has both innate scientific interest and is of societal importance due to our reliance on Earth resources. Unfortunately, seismic wavefield simulation is extremely computationally expensive, often requiring the resources of entire supercomputer facilities to run, which hinders our ability to use seismic information. The objective of this project was to investigate machine learning based methods for accelerating seismic wavefield computations, in particular for ground motion studies of seismic hazard. We chose to investigate the recently developed physics-informed neural-network (PINN) approach, as unlike traditional machine learning methods, PINNs do not require reference data for machine learning, instead relying on our knowledge of underlying physical principles.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
In regions of high seismicity, it is essential for society to understand the associated seismic hazard. A cornerstone of seismic hazard assessment is the ability to predict what kind of ground shaking occurs from a particular type of source at some given location; however, given the immense expense of full 3D viscoelastic seismic wavefield simulations, researchers typically rely on empirical relations that do not capture the path effects of wave propagation, which can significantly increase ground shaking by waveguiding and other effects. I propose to use a novel development in scientific machine learning - Physics Informed Neural Networks (PINNs) - to solve the 3D viscoelastic wavefield propagation problem. A fully trained network will drastically reduce the time required to compute the seismic response for arbitrary sources and receivers, enabling fully physics based seismic hazard in a probabilistic framework. PINNs utilize our knowledge of the physics, in this case the equations of motion for continuous media, to regularize learning, which reduces the required amount of training data by many orders of magnitude. We will utilize the PINN wavefield solver in a testbed study of physics based seismic hazard assessment for Southern California, with the goal of producing a framework that is computationally accessible to apply across the world.
Оригинален текст от CORDIS (на английски).
Участници
- THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD · OxfordКоординаторОбединеното кралство
Връзки
Данни: CORDIS, © Европейски съюз