16.100 Aerodynamics, Fall 2002 by Darmofal, David L.

Subjects: Get full text

Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation by Abdullah, null, Faye, Ibrahima, Laila Amera, Aziz

“…Physics-informed neural networks (PINN) are an artificial neural network (ANN) approach for solving differential equations. …”
Get full text

AI is a viable alternative to high throughput screening: a 318-target study by Wallach, Izhar, Bernard, Denzil, Nguyen, Kong, Ho, Gregory, Morrison, Adrian, Stecula, Adrian, Rosnik, Andreana, O’Sullivan, Ann Marie, Davtyan, Aram, Samudio, Ben, Thomas, Bill, Worley, Brad, Butler, Brittany, Laggner, Christian, Thayer, Desiree, Moharreri, Ehsan, Friedland, Greg, Truong, Ha, van den Bedem, Henry, Ng, Ho Leung, Stafford, Kate, Sarangapani, Krishna, Giesler, Kyle, Ngo, Lien, Mysinger, Michael, Ahmed, Mostafa, Anthis, Nicholas J., Henriksen, Niel, Gniewek, Pawel, Eckert, Sam, de Oliveira, Saulo, Suterwala, Shabbir, PrasadPrasad, Srimukh Veccham Krishna, Shek, Stefani, Contreras, Stephanie, Hare, Stephanie, Palazzo, Teresa, O’Brien, Terrence E., Van Grack, Tessa, Williams, Tiffany, Chern, Ting-Rong, Kenyon, Victor, Lee, Andreia H., Cann, Andrew B., Bergman, Bastiaan, Anderson, Brandon M., Cox, Bryan D., Warrington, Jeffrey M., Sorenson, Jon M., Goldenberg, Joshua M., Young, Matthew A., DeHaan, Nicholas, Pemberton, Ryan P., Schroedl, Stefan, Abramyan, Tigran M., Gupta, Tushita, Mysore, Venkatesh, Presser, Adam G., Ferrando, Adolfo A., Andricopulo, Adriano D., Ghosh, Agnidipta, Ayachi, Aicha Gharbi, Mushtaq, Aisha, Shaqra, Ala M., Toh, Alan Kie Leong, Smrcka, Alan V., Ciccia, Alberto, de Oliveira, Aldo Sena, Sverzhinsky, Aleksandr, de Sousa, Alessandra Mara, Agoulnik, Alexander I., Kushnir, Alexander, Freiberg, Alexander N., Statsyuk, Alexander V., Gingras, Alexandre R., Degterev, Alexei, Tomilov, Alexey, Vrielink, Alice, Garaeva, Alisa A., Bryant-Friedrich, Amanda, Caflisch, Amedeo, Patel, Amit K., Rangarajan, Amith Vikram, Matheeussen, An, Battistoni, Andrea, Caporali, Andrea, Chini, Andrea, Ilari, Andrea, Mattevi, Andrea, Foote, Andrea Talbot, Trabocchi, Andrea, Stahl, Andreas, Herr, Andrew B., Berti, Andrew, Freywald, Andrew, Reidenbach, Andrew G., Lam, Andrew, Cuddihy, Andrew R., White, Andrew, Taglialatela, Angelo

“…We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. …”
Get full text

Data-driven forward and inverse analysis of two-dimensional soil consolidation using physics-informed neural network by Wang, Yu, Shi, Chao, Shi, Jiangwei, Lu, Hu

“…Different random seeds are used to test the robustness of the PINN developed and quantify the associated model uncertainty. …”
Get full text

Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool by Mukhmetov, Olzhas, Zhao, Yong, Mashekova, Aigerim, Zarikas, Vasilios, Ng, Eddie Yin Kwee, Aidossov, Nurduman

“…This work presents the development of a novel Physics-Informed Neural Network (PINN) method for fast forward simulation of heat transfer through cancerous breast models. …”
Get full text

Range expansion of a declining forest species, the Western Gray Squirrel (Sciurus griseus), into semi-arid woodland by Sultaire, SM, Montgomery, RA, Jackson, PJ, Millspaugh, JJ

“…Occupancy modeling revealed that western gray squirrels were equally likely to occur in piñon–juniper woodland compared to mature pine forest that they occupy elsewhere in their range. …”

Virtual palpation by Luo, Ting.

“…By defining a proper formula to generate the force feedback, user could feel like touching the real skin and view the skin deformation when touching or moving on the skin, and by performing palpation, user could feel the doubtful organs in abdomen area like liver and spleen.…”
Get full text

Physics-guided neural network for tissue optical properties estimation by Chong, Kian Chee, Pramanik, Manojit

“…Finding the optical properties of tissue is essential for various biomedical diagnostic/therapeutic applications such as monitoring of blood oxygenation, tissue metabolism, skin imaging, photodynamic therapy, low-level laser therapy, and photo-thermal therapy. …”
Get full text

Predictive modeling and optimization of noise emissions in a palm oil methyl ester-fueled diesel engine using response surface methodology and artificial neural network integrated... by Muhammad Zikri, Japri, Mohd Shahrir, Mohd Sani, Mohd Fadzil Faisae, Ab Rashid, Muriban, Jackly, Galang Sandy, Prayogo

“…These findings endorse the feasibility of applying ANN-GA in scenarios where precise noise prediction is crucial, particularly in relation to alternative fuels like POME.…”
Get full text

A paper-based glucose sensoring microneedle patch by Zheng, Shu

“…By diagnose this index can detect many diseases like diabetes. However, the current most methods of clinical analysis of the glucose level need to pierce skin to take blood sample or takes a long time to analyze which will cause skin infection and uncomfortable of patients. …”
Get full text

Modular Morphing Lattices for Large-Scale Underwater Continuum Robotic Structures by Parra Rubio, Alfonso, Fan, Dixia, Jenett, Benjamin, del Águila Ferrandis, José, Tourlomousis, Filippos, Abdel-Rahman, Amira, Preiss, David, Zemánek, Jiri, Triantafyllou, Michael, Gershenfeld, Neil

“…The first is a morphing beam snake-like robot that can generate thrust at specific anguilliform swimming parameters. …”
Get full text

Artificial neural network-salp-swarm algorithm for stock price prediction by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Azlan, Abdul Aziz

“…Additionally, the SSA-ANN model is compared with other two hybrid models: the ANN optimized by the Whale Optimization Algorithm (WOA-ANN) and Moth-Flame Optimizer (MOA-ANN), as well as a single model, namely the Autoregressive Integrated Moving Average (ARIMA). …”
Get full text

Physics-Informed Deep Learning for Plasma Etch Optimization by Dighamber, Mohit

“…Future work includes utilizing the PINN model in a Bayesian framework to facilitate recipe optimization for the desired etch profile.…”
Get full text

Fuzzy associative memory architecture by Ting, Chan Wai

Get full text

Design & fabrication of an apparatus to simulate the dynamic pressure of human heart by Zheng, Jinming

“…When discussing loads and injury, many do not think about injury to the skin and its underlying tissues. Just like we are operating machine, in order to keep the system operating, every component serves a specific function. just like a mechanical system, there are valves, filters, wiring and biological pumps. . …”
Get full text

Prediction of rainfall-runoff processes through black-box techniques by Lee, Liang Cen

“…Levenberg-Marquardt Backpropagation (LMBP) was used as training algorithm in ANN model. In order to generate an optimum prediction, different numbers and combinations of data were used as inputs to maximize the efficiency of ANN model. …”
Get full text

What does acne genetics teach us about disease pathogenesis? by Common, J. E. A., Barker, J. N., van Steensel, Maurice A. M.

“…Background: Acne vulgaris is a highly prevalent inflammatory skin disorder with a complex pathogenesis, characterized by comedones, papules, pustules and nodules. …”
Get full text

Aerogel and ionogel strain sensors by Tan, Melissa Siew Ting

“…Scientists have been interested in mimicking these properties of skin using synthetic materials to create electronic-skin. …”
Get full text

Investigating the Illusion of Wetness: Cold Dry Stimuli in Sensory Perception by Ozor-Ilo, Ozioma

“…Humans lack specialized receptors for perceiving wetness and so it is a compound sensation based on changes in skin temperature and contact pressure that are sensed by thermoreceptors and mechanoreceptors in the skin. …”
Get full text
Get full text

Data-driven power system stability assessment by Kang, Hongyu

“…Through experiments and data results visualization, the accuracy of DT is obtained as 0.9953, SVM as 0.9967 and ANN as 0.9968. Meanwhile, the ROC and AUC curves of ANN and SVM are close to the upper-left intersection, which proves that the model achievement is good. …”
Get full text