Results 101 to 110 of about 2,291 (214)

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, Volume 123, Issue 6, Page 1465-1480, June 2026.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source

Burgers' PINNs with implicit Euler Transfer Learning

Revista Mundi Engenharia, Tecnologia e Gestão (ISSN: 2525-4782)
Burgers’ equation is a well-established test case in mathematical analysis and numerical simulations of convective-diffusive Partial Differential Equations (PDEs). In our study, we focus on the viscous Burgers equation with initial and Dirichlet boundary conditions.
Vitória Biesek, Pedro Henrique de Almeida Konzen   +1 more
openaire   +2 more sources

Physics‐Informed Neural Networks for Predictive Modeling and Optimization of pH‐Triggered Drug Release from Quercetin‐Loaded Nanocomposites

ChemNanoMat, Volume 12, Issue 6, June 2026.
A physics‐informed neural network enables accurate and mechanistically consistent prediction of quercetin release from nanocomposites. Precise control of drug‐release kinetics remains a major challenge in nanomedicine, particularly for pH‐responsive delivery systems targeting the acidic tumor microenvironment. Here, we develop a physics‐informed neural
Abbas Rahdar, Sonia Fathi‐karkan, M. Ali Aboudzadeh   +2 more
wiley   +1 more source

Understanding the Difficulty of Solving Cauchy Problems with PINNs

CoRR
Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in recent years. However, they often fail to achieve the same level of accuracy as classical methods in solving differential equations. In this paper, we identify two sources of this issue in the case of Cauchy problems: the use of $L^2$ residuals as objective ...
Tao Wang, Bo Zhao 0028, Sicun Gao, Rose Yu   +3 more
openaire   +3 more sources

Defense Algorithm Based on Reinforcement Learning and Digital Twin for Distribution Protection Devices Adversarial Samples

Engineering Reports, Volume 8, Issue 6, June 2026.
Power system protection devices are vulnerable to adversarial samples. We propose a digital twin and reinforcement learning framework. It trains a virtual model within safe operational boundaries, reducing the false operation rate to below 3.5%. The model's inference delay is under 10 ms, meeting real‐time protection requirements.
Wei Zhang, Shuai Zhang, Song Wang, Zheran Zheng   +3 more
wiley   +1 more source

Research on SCO2 Critical Flow Prediction Model Based on PINN-HEM Hybrid Framework

Yuanzineng kexue jishu
The accurate prediction of critical flow in supercritical carbon dioxide (SCO2) reactor systems is a crucial aspect of pressure drop accident safety analysis.
WANG Xinyu1, WANG Siyuan1, YANG Jun2, CHEN Lilian1, LYU Junhong1, ZHANG Xuan1, KANG Yanjie1, YUAN Yuan1, ZHOU Yuan1
doaj   +1 more source

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-Informed Neural Networks Framework for Interface Problems

Communications in Computational Physics
17 pages, 8 figures, 6 ...
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Antareep Kumar Sarma   +3 more
openaire   +2 more sources

A Fuzzy‐Logic and Machine Learning‐Based Framework for Water Quality Index Classification in Extensive and Intensive Aquaculture Systems

Engineering Reports, Volume 8, Issue 6, June 2026.
This study presents an Internet of Things (IoT)‐to‐Cloud framework for real‐time monitoring and prediction of the Water Quality Index (WQI) across both extensive and intensive aquaculture systems. By integrating Fuzzy Logic biological thresholds with ML classification, the Random Forest model achieves over 99.5% accuracy in both environments ...
Mohammod Abul Kashem, Mehedi Hasan Shuvo, Shabnom Mustary, Md. Mithun Ali, Nayeem Ullah, Md. Delwar Hossain, Md. Asaduzzaman Chowdhury, Jia Uddin   +7 more
wiley   +1 more source

Audit‐Ready Machine Learning for Short‐Horizon Equity Prediction: A Dual‐Target Benchmark With Fold‐Isolated Preprocessing

Engineering Reports, Volume 8, Issue 6, June 2026.
This study introduces a rigorous, walk‐forward protocol to evaluate next‐day return and volatility‐proxy forecasting across matched model families. By enforcing fold‐isolated preprocessing and causal feature construction on US mega‐caps, the study eLectively mitigates performance inflation.
Abdul Kadar Muhammad Masum, Md. Abul Kalam Azad, Mirza Nadim Saad, Al‐Amain, Mohammad Azam Khan, A. B. M. Yasir Arafat   +5 more
wiley   +1 more source

A Gradient-Variance Weighting Physics-Informed Neural Network for Solving Integer and Fractional Partial Differential Equations

Applied Sciences
Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving partial differential equations (PDEs) by embedding physical laws into the learning process.
Liang Zhang, Quansheng Liu, Ruigang Zhang, Liqing Yue, Zhaodong Ding   +4 more
doaj   +1 more source