Ján Drgoňa

@jan_drgona

Associate professor @JohnsHopkins, data scientist @PNNLab. Formerly at @KU_Leuven, @ClimateChangeAI. #SciML #PIML #control #energy #sustainability

Richland

drgona.github.io

Joined April 2015

1,009 Following

1,118 Followers

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Ján Drgoňa
@jan_drgona
Sep 18, 2024
Exciting PhD and Postdoc Opportunities at Johns Hopkins University! I’m thrilled to announce that I will be joining the Johns Hopkins Department of Civil and Systems Engineering and the Ralph O'Connor Sustainable Energy Institute (ROSEI) as an Associate Professor in January
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Ján Drgoňa
@jan_drgona
Jul 10
The recordings of our L4DC 2026 Scientific Machine Learning (SciML) tutorial are now available on YouTube. This tutorial presented a unified abstraction for Scientific Machine Learning (SciML), covering methodologies for learning to model, optimize, and control by integrating
Tutorial 2 (Part 1): Scientific Machine Learning for Modeling, Optimization, and Control
From youtube.com
Ján Drgoňa
@jan_drgona
Jun 16
If you are attending the L4DC conference at USC, join us tomorrow morning for a half-day tutorial on Scientific Machine Learning for Modeling, Optimization, and Control. We'll cover the fundamentals of SciML through hands-on coding examples, including learning to model, learning
Ján Drgoňa
@jan_drgona
Jun 2
Excited to share that our paper "CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control" has been accepted to ICML 2026. 🎉 This work was led by my first-year PhD student Pietro Zanotta and introduces the Cardinality-Invariant Neural Operator Control
arxiv.org
CINOC: Cardinality-Invariant Neural Operator Policies for Scalable...
Controlling partial differential equations (PDEs) with learning-based policies remains fundamentally limited by fixed-dimensional representations: policies trained for a specific sensor, actuator,...
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Ján Drgoňa
@jan_drgona
May 26
I am at the American Control Conference in New Orleans. I will be speaking today at the PDE-AI workshop about our recent work on using neural operators and differentiable predictive control for partial differential equations. Workshop website: …c-2026-ai-for-pde-workshop.vercel.app Talk info:
acc-2026-ai-for-pde-workshop.vercel.app
PDE-AI Workshop

Ján Drgoňa

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