Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …
providing an expressive view of the chemical space and multiscale processes. Their …
Discussing the spectra of physics-enhanced machine learning via a survey on structural mechanics applications
The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …
Symplectic spectrum Gaussian processes: learning Hamiltonians from noisy and sparse data
Hamiltonian mechanics is a well-established theory for modeling the time evolution of
systems with conserved quantities (called Hamiltonian), such as the total energy of the …
systems with conserved quantities (called Hamiltonian), such as the total energy of the …
MBD-NODE: physics-informed data-driven modeling and simulation of constrained multibody systems
J Wang, S Wang, HM Unjhawala, J Wu… - Multibody System …, 2024 - Springer
We describe a framework that can integrate prior physical information, eg, the presence of
kinematic constraints, to support data-driven simulation in multibody dynamics. Unlike other …
kinematic constraints, to support data-driven simulation in multibody dynamics. Unlike other …
Symmetry preservation in Hamiltonian systems: Simulation and Learning
This work presents a general geometric framework for simulating and learning the dynamics
of Hamiltonian systems that are invariant under a Lie group of transformations. This means …
of Hamiltonian systems that are invariant under a Lie group of transformations. This means …
Learning deep dissipative dynamics
This study challenges strictly guaranteeing``dissipativity''of a dynamical system represented
by neural networks learned from given time-series data. Dissipativity is a crucial indicator for …
by neural networks learned from given time-series data. Dissipativity is a crucial indicator for …
Estimating the energy of dissipative neural systems
There is, at present, a lack of consensus regarding precisely what is meant by the
term'energy'across the sub-disciplines of neuroscience. Definitions range from deficits in the …
term'energy'across the sub-disciplines of neuroscience. Definitions range from deficits in the …
Latent-Energy-Based NNs: An interpretable Neural Network architecture for model-order reduction of nonlinear statics in solid mechanics
Nonlinear mechanical systems can exhibit non-uniqueness of the displacement field in
response to a force field, which is related to the non-convexity of strain energy. This work …
response to a force field, which is related to the non-convexity of strain energy. This work …
Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications
The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …