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Demo Data from EDBT 2026 / reviewers in the wild / expert

Ganesh Sahadeo Meshram

dblp:429/6297 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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.

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

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

TopicWeightPapersLastEvidence papers
Computational science and engineering › computational fluid dynamics
multiphase flow simulation		 1.012026
Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract) · AAAI 2026
Machine learning › Deep learning architectures and training
physics-informed neural network		 0.312026
Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract) · AAAI 2026

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

physics-informed neural networks · 2.0
YearPublicationVenuePosition
2026 Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract)
abstract
This study develops a physics-informed neural network (PINN) framework to predict droplet spreading dynamics on unstructured rough surfaces. The trained model effectively captures temporal evolution of the droplet shape, contact line motion, and interfacial deformation. This integration of multiphase physics with neural networks provides a mesh-free and computationally efficient alternative to numerical solvers, enabling rapid analysis and design of wettability-controlled surfaces, microfluidic devices.
Ganesh Sahadeo Meshram, P. P. Chakrabarti 0001
 AAAI1