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Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems
Predicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales....
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Decentralized digital twins of complex dynamical systems
In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and...
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Physics-informed neural ODE (PINODE): embedding physics into models using collocation points
Building reduced-order models (ROMs) is essential for efficient forecasting and control of complex dynamical systems. Recently, autoencoder-based...
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Physics-aware differentiable design of magnetically actuated kirigami for shape morphing
Shape morphing that transforms morphologies in response to stimuli is crucial for future multifunctional systems. While kirigami holds great promise...
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Spatiotemporal organization of ant foraging from a complex systems perspective
We use complex systems science to explore the emergent behavioral patterns that typify eusocial species, using collective ant foraging as a...
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Quantum physics in connected worlds
Theoretical research into many-body quantum systems has mostly focused on regular structures which have a small, simple unit cell and where a...
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Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems
Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional...
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Extrapolating tip** points and simulating non-stationary dynamics of complex systems using efficient machine learning
Model-free and data-driven prediction of tip** point transitions in nonlinear dynamical systems is a challenging and outstanding task in complex...
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Bell correlations outside physics
Correlations are ubiquitous in nature and their principled study is of paramount importance in scientific development. The seminal contributions from...
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Effects of temperature and magnetization on the Mott–Anderson physics in one-dimensional disordered systems
We investigate the Mott–Anderson physics in interacting disordered one-dimensional chains through the average single-site entanglement quantified by...
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Toward a formal theory for computing machines made out of whatever physics offers
Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing....
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Solving real-world optimization tasks using physics-informed neural computing
Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario...
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3D multi-physics uncertainty quantification using physics-based machine learning
Quantitative predictions of the physical state of the Earth’s subsurface are routinely based on numerical solutions of complex coupled partial...
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Fast gradient algorithm for complex ICA and its application to the MIMO systems
This paper proposes a new gradient-descent algorithm for complex independent component analysis and presents its application to the Multiple-Input...
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Physics to system-level modeling of silicon-organic-hybrid nanophotonic devices
The continuous growth in data volume has sparked interest in silicon-organic-hybrid (SOH) nanophotonic devices integrated into silicon photonic...
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Network structure from a characterization of interactions in complex systems
Many natural and man-made complex dynamical systems can be represented by networks with vertices representing system units and edges the coupling...
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Physics-informed shape optimization using coordinate projection
The rapid growth of artificial intelligence is revolutionizing classical engineering society, offering novel approaches to material and structural...
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Data-driven predictions of complex organic mixture permeation in polymer membranes
Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing...
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Experimentally realized physical-model-based frugal wave control in metasurface-programmable complex media
Metasurface-programmable radio environments are considered a key ingredient of next-generation wireless networks. Yet, identifying a metasurface...
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Physics-informed reinforcement learning for motion control of a fish-like swimming robot
Motion control of fish-like swimming robots presents many challenges due to the unstructured environment and unmodelled governing physics of the...
