| 1 |
RETRACTION: Application of machine learning for filtered density function closure in MILD combustion (Retraction of Vol 225, Pg 160, 2021)
Chen ZX, Iavarone S, Ghiasi G, Kannan V, D'Alessio G, Parente A, Swaminathan N
Combustion and Flame, 225, 160, 2021 |
| 2 |
Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework
Mahmoudabadbozchelou M, Caggioni M, Shahsavari S, Hartt WH, Karniadakis GE, Jamali S
Journal of Rheology, 65(2), 179, 2021 |
| 3 |
A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system
Kim JW, Park BJ, Yoo H, Oh TH, Lee JH, Lee JM
Journal of Process Control, 87, 166, 2020 |
| 4 |
A deep neural network approach for behind-the-meter residential PV size, tilt and azimuth estimation
Mason K, Reno MJ, Blakely L, Vejdan S, Grijalva S
Solar Energy, 196, 260, 2020 |
| 5 |
Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal
Qin Y, Li K, Liang ZH, Lee B, Zhang FY, Gu YC, Zhang L, Wu FZ, Rodriguez D
Applied Energy, 236, 262, 2019 |
| 6 |
A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network
Wang SX, Chen HW
Applied Energy, 235, 1126, 2019 |
| 7 |
Uncertainty quantification in three dimensional natural convection using polynomial chaos expansion and deep neural networks
Shahane S, Aluru NR, Vanka SP
International Journal of Heat and Mass Transfer, 139, 613, 2019 |
| 8 |
Synaptic device using a floating fin-body MOSFET with memory functionality for neural network
Woo SY, Choi KB, Lim S, Lee ST, Kim CH, Kang WM, Kwon D, Bae JH, Park BG, Lee JH
Solid-State Electronics, 156, 23, 2019 |
| 9 |
Forecasting day-ahead electricity prices in Europe: The importance of considering market integration
Lago J, De Ridder F, Vrancx P, De Schutter B
Applied Energy, 211, 890, 2018 |
| 10 |
State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
Chemali E, Kollmeyer PJ, Preindl M, Emadi A
Journal of Power Sources, 400, 242, 2018 |
