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Ridwan Jalali

dblp:322/5288 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Graph learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning
graph network simulators
0.612022
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator · KDD 2022
Machine learning › Graph learning
graph neural network
0.612022
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator · KDD 2022

Methods — techniques the papers use, named apart from their topics

surrogate modeling · 1.1hybrid graph network · 1.1finite difference simulation · 1.1
YearPublicationVenuePosition
2022 Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
abstract
Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast and accurate models are needed for high-stake decision making, for example, for well placement optimization and field development planning. Classical finite difference numerical simulators require massive computational resources to model large-scale real-world reservoirs. Alternatively, streamline simulators and data-driven surrogate models are computationally more efficient by relying on approximate physics models, however they are insufficient to model complex reservoir dynamics at scale.
Tailin Wu, Qinchen Wang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec
KDD7