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Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena

Project description

Creating better numerical algorithms with data, theory, and optimisation

The explosion of access to big data in virtually every field, combined with the increasing power of high-performance computing, seems to offer limitless capabilities for animating and simulating complex natural systems. However, developing the numerical algorithms to incorporate all the data and represent it is a challenge. Furthermore, physical details at the finest scales are still out of reach, even with all of the data in the world. The EU-funded CoDiNA project is developing novel theoretical tools, employing powerful optimisation techniques, and conducting a huge number of smaller simulations to reveal trends that harbour larger aggregate system behaviours. These ideas will lead to novel numerical algorithms that are optimised for accuracy and efficiency on any hardware.

Objective

Modern numerical algorithms and high performance computing systems are capable of simulating physical systems far more efficiently than those from just few years ago. However, this recent increase in computational power comes with an insatiable demand for complexity and scale: scientists and artists now wish to model textiles at the level of individual fibers and simulate oceans down to the smallest ripple. The typical strategy of analytically deriving integrators and hand-tuning parameters will inevitably fail to cope with the increasing non-linearity and complexity of these problems. This shift in problem complexity necessitates entirely novel strategies for discovering numerical algorithms.

We will redefine the state of the art in numerical simulation and animation by combining three different approaches: First, we will develop analytical tools customized for large scales; we will derive optimal numerical algorithms by viewing physics through the lens of computational complexity, and by observing limiting behaviors as problems increase in size. Next, we will gain physical insights by simulating huge numbers of smaller simulations and generating large data sets. By discovering trends in this data, we will faithfully approximate aggregate behaviors in systems that are far too complex for analytical techniques to penetrate. Finally, by framing the derivation of numerical algorithms as a constrained optimization problem, we will be able to deliver provably optimal code for a given piece of hardware and precisely control accuracy/efficiency trade-offs.

The combination of these research directions will enable efficient simulations of massively complicated systems that are currently unfeasible to compute. Due to the timely and groundbreaking nature of these proposed directions, we also expect to develop entirely unprecedented methods for physics simulation and discover a number of theoretical insights and scientific advances along the way.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2021-COG

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Host institution

INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 936 503,00
Address
Am Campus 1
3400 KLOSTERNEUBURG
Austria

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Region
Ostösterreich Niederösterreich Wiener Umland/Nordteil
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 936 503,00

Beneficiaries (1)

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