Learning quantum systems
The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …
quantum systems of increasing complexity, with key applications in computation, simulation …
Interdisciplinary research in artificial intelligence: challenges and opportunities
The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple
digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable …
digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
Physics-informed learning of governing equations from scarce data
Harnessing data to discover the underlying governing laws or equations that describe the
behavior of complex physical systems can significantly advance our modeling, simulation …
behavior of complex physical systems can significantly advance our modeling, simulation …
Physics-informed deep learning for computational elastodynamics without labeled data
Numerical methods such as finite element have been flourishing in the past decades for
modeling solid mechanics problems via solving governing partial differential equations …
modeling solid mechanics problems via solving governing partial differential equations …
[HTML][HTML] Unsupervised discovery of interpretable hyperelastic constitutive laws
We propose a new approach for data-driven automated discovery of isotropic hyperelastic
constitutive laws. The approach is unsupervised, ie, it requires no stress data but only …
constitutive laws. The approach is unsupervised, ie, it requires no stress data but only …
Digital twin: Values, challenges and enablers
A digital twin can be defined as an adaptive model of a complex physical system. Recent
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data
The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the
discovery of parsimonious dynamic models and governing equations from time-series data …
discovery of parsimonious dynamic models and governing equations from time-series data …
Inverse Dirichlet weighting enables reliable training of physics informed neural networks
We characterize and remedy a failure mode that may arise from multi-scale dynamics with
scale imbalances during training of deep neural networks, such as physics informed neural …
scale imbalances during training of deep neural networks, such as physics informed neural …
Hypersolvers: Toward fast continuous-depth models
The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the
search for novel dynamical system-inspired deep learning primitives; however, their …
search for novel dynamical system-inspired deep learning primitives; however, their …