From the 1 of 7 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
7 papers
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
Karin Yu, Eleni Chatzi, Georgios Kissas
The paper presents Latent Grammar Flow, a neuro‑symbolic generative framework that embeds differential equations as grammar‑based tokens in a discrete latent space and uses a discr…
Neuro-Symbolic AI for Analytical Solutions of Differential Equations
Orestis Oikonomou, Levi Lingsch, Dana Grund +2
Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert intuition or exhaustive search o…
Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Science
Levi Lingsch, Georgios Kissas, Johannes Jakubik +1
Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and ge…
Grammar-based Ordinary Differential Equation Discovery
Karin L. Yu, Eleni Chatzi, Georgios Kissas
The understanding and modeling of complex physical phenomena through dynamical systems has historically driven scientific progress, as it provides the tools for predicting the beha…
Accelerated Patient-Specific Calibration via Differentiable Hemodynamics Simulations
Diego Renner, Georgios Kissas
One of the goals of personalized medicine is to tailor diagnostics to individual patients. Diagnostics are performed in practice by measuring quantities, called biomarkers, that in…
FUSE: Fast Unified Simulation and Estimation for PDEs
Levi E. Lingsch, Dana Grund, Siddhartha Mishra +1
The joint prediction of continuous fields and statistical estimation of the underlying discrete parameters is a common problem for many physical systems, governed by PDEs. Hitherto…