Research graph
References from Physics-informed hybrid neural networks with data constraints: A case study on lorenz63. Local targets link to admitted publications; unresolved targets remain external evidence.
Towards physically-consistent, data-driven models of convection
2020 · External reference
10.1103/physrevlett.126.098302
10.1103/physrevlett.126.098302 · External reference
An extension of the Lyapunov analysis for the predictability problem
10.1175/1520-0469(1998)055<3409:aeotla>2.0.co;2 · 1998 · External reference
Physics-informed neural networks (PINNs) for fluid mechanics: a review
10.1007/s10409-021-01148-1 · 2021 · External reference
Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: Reservoir computing, artificial neural network, and long short-term memory network
10.5194/npg-27-373-2020 · 2020 · External reference
CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
10.1016/j.cma.2022.114909 · 2022 · External reference
Scientific machine learning through physics–informed neural networks: Where we are and what’s next
10.1007/s10915-022-01939-z · 2022 · External reference
CoPhy -PGNN: Learning physics-guided neural networks with competing loss functions for solving eigenvalue problems
10.1145/3530911 · 2022 · External reference
On the recurrence and robust properties of lorenz’63 model
10.1007/s00220-012-1438-7 · 2012 · External reference
Unresolved reference
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Deep learning for multi-year ENSO forecasts
10.1038/s41586-019-1559-7 · 2019 · External reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
An efficient numerical scheme for burgers' equation
10.1016/s0096-3003(97)10060-1 · 1998 · External reference
10.22541/essoar.173869623.33180280/v2
10.22541/essoar.173869623.33180280/v2 · External reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · 2021 · External reference
Data-driven learning of chaotic dynamical systems using discrete-temporal sobolev networks
10.1016/j.neunet.2024.106152 · 2024 · External reference
hp-VPINNs: Variational physics-informed neural networks with domain decomposition
10.1016/j.cma.2020.113547 · 2021 · External reference
DPM: A novel training method for physics-informed neural networks in extrapolation
2020 · External reference
Unresolved reference
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Characterizing possible failure modes in physics-informed neural networks
2021 · External reference
Comparative study of Lorenz model based ensemble forecasting and single forecasting
2018 · External reference
Forecasting the indian ocean dipole with deep learning techniques
2021 · External reference
Deterministic nonperiodic flow
10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2 · 1963 · External reference
Physics-informed neural networks with hard constraints for inverse design
2021 · External reference
Self-Adaptive physics-informed neural networks using a soft attention mechanism
10.1016/j.jcp.2022.111722 · 2023 · External reference
Estimates on the generalization error of physics informed neural networks (PINNs) for approximating a class of inverse problems for PDEs
2023 · External reference
Embedding hard physical constraints in neural network coarse-graining of 3D turbulence
2020 · External reference
Modelling the dynamics of nonlinear time series using canonical variate analysis
10.1016/s0167-2789(02)00534-1 · 2002 · External reference
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · 2019 · External reference
Unresolved reference
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Solving real-world optimization tasks using physics-informed neural computing
10.1038/s41598-023-49977-3 · 2024 · External reference
On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
10.4208/cicp.oa-2020-0193 · 2020 · External reference
Enhanced physics-informed neural networks with augmented lagrangian relaxation method (AL-PINNs)
2023 · External reference
Unresolved reference
2023 · External reference
Optimally weighted loss functions for solving PDEs with neural networks
10.1016/j.cam.2021.113887 · 2022 · External reference
Unresolved reference
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A practical PINN framework for multi-scale problems with multi-magnitude loss terms
10.1016/j.jcp.2024.113112 · 2024 · External reference
Determining Lyapunov exponents from a time series
10.1016/0167-2789(85)90011-9 · 1985 · External reference
Self-adaptive loss balanced Physics-informed neural networks
10.1016/j.neucom.2022.05.015 · 2022 · External reference
Analysis of attractor behavior and predictability in a coupled lorenz model
2023 · External reference
An adaptive optimal interpolation based on analog forecasting: Application to SSH in the gulf of mexico
10.1175/jtech-d-20-0001.1 · 2020 · External reference
