Results 111 to 120 of about 2,349 (245)

Physics-informed Neural Networks for Biopharma Applications

, 2021
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds.
Cedergren, Linnéa
core  

AI in chemical engineering: From promise to practice

AIChE Journal, Volume 72, Issue 7, July 2026.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

PINN-BO: A Black-Box Optimization Algorithm Using Physics-Informed Neural Networks

PINN-BO: A Black-Box Optimization Algorithm Using Physics-Informed Neural ...
Sunil Gupta (13096512), Alistair Shilton (13059261), Dat Phan-Trong (19780500), Hung The Tran (19780503)   +3 more
core  

Physics‐Informed Neural Networks for Modeling the Martian Induced Magnetosphere

Geophysical Research Letters, Volume 53, Issue 11, 16 June 2026.
Abstract Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics‐based, relying on computationally intensive simulations. For the first time, we
Jiawei Gao, Chuanfei Dong, Chi Zhang, Yilan Qin, Simin Shekarpaz, Xinmin Li, Liang Wang, Hongyang Zhou, Abigail Tadlock   +8 more
wiley   +1 more source

Seeing Through Scattering With Computational Advances: A Review

Advanced Photonics Research, Volume 7, Issue 6, June 2026.
In scattering media, light scrambles into random speckles and impedes our vision. Unlocking hidden information enables breakthroughs to see behind the opaqueness, inspiring applications in imaging, communication, and encryption. Unlike clear media such as clear water and air, a scattering medium is inhomogeneous, in which propagating photons are ...
Huanhao Li, Kai Chen, Xueli Chen, Puxiang Lai, Zhipeng Yu   +4 more
wiley   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, Volume 2, Issue 3, June 2026.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Leveraging Physics with Deep Learning: Physics-Informed Neural Networks (PINN) for IRI Prediction in Flexible Pavements

This study presents a Physics-Informed Neural Network (PINN) framework for predicting the IRI of flexible pavements, utilizing the LTPP database. A total of 390 observations from 74 pavement sections across the United States were utilized.
Ahmed, Tanvir, Isied, Mayzan M., Souliman, Mena I.   +2 more
core   +1 more source

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

Physics-Informed Neural Networks for Process Optimization in Laser Powder Bed Fusion of Inconel 718 Superalloy: A Data-Efficient, Physics-Constrained Machine Learning Framework

Metals
This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fusion
Saurabh Tiwari, Seong Jun Heo, Nokeun Park   +2 more
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