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Found 10 papers, 3 papers with code
A Learnable Prior Improves Inverse Tumor Growth Modeling
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients.
Generative Learning for Forecasting the Dynamics of Complex Systems
We introduce generative models for accelerating simulations of complex systems through learning and evolving their effective dynamics.
Closure Discovery for Coarse-Grained Partial Differential Equations using Multi-Agent Reinforcement Learning
However, simulations that capture the full range of spatio-temporal scales in such PDEs are often prohibitively expensive.
Interpretable learning of effective dynamics for multiscale systems
In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data.
Interpretable reduced-order modeling with time-scale separation
To this end, we combine a non-linear autoencoder architecture with a time-continuous model for the latent dynamics in the complex space.
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
Neural Operators offer a powerful, data-driven tool for solving parametric PDEs as they can represent maps between infinite-dimensional function spaces.
Physics-enhanced Neural Networks in the Small Data Regime
Identifying the dynamics of physical systems requires a machine learning model that can assimilate observational data, but also incorporate the laws of physics.
Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime
The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems.
Physics-aware, probabilistic model order reduction with guaranteed stability
Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model that is predictive of the fine-grained system's long-term evolution but also of its behavior under different initial conditions.
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems.
