{"id":62737,"date":"2025-04-12T13:25:10","date_gmt":"2025-04-12T13:25:10","guid":{"rendered":""},"modified":"2025-04-12T13:25:10","modified_gmt":"2025-04-12T13:25:10","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62737","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62737\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Inversion-Deeponet: \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 Deeponet \u0631\u0645\u0627\u0646 \u0628\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u062f\u06a9\u0648\u0631\u0627\u0633\u06cc\u0648\u0646 \u0628\u0631\u0627\u06cc \u0648\u0627\u0631\u0648\u0646\u06af\u06cc \u06a9\u0627\u0645\u0644 \u0634\u06a9\u0644"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nInversion-DeepONet: A Novel DeepONet-Based Network with Encoder-Decoder for Full Waveform Inversion<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Inversion-Deeponet: \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 Deeponet \u0631\u0645\u0627\u0646 \u0628\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u062f\u06a9\u0648\u0631\u0627\u0633\u06cc\u0648\u0646 \u0628\u0631\u0627\u06cc \u0648\u0627\u0631\u0648\u0646\u06af\u06cc \u06a9\u0627\u0645\u0644 \u0634\u06a9\u0644<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nZekai Guo, Lihui Chai, Shengjun Huang, Ye Li<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n16<\/td>\n<\/tr>\n
\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nMachine Learning,\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 15 August, 2024; originally announced August 2024.<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 15 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f.<\/td>\n<\/tr>\n
\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc <\/td>\n\n INSPIRE HEP<\/a>
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\n NASA ADS<\/a>
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\n Google Scholar<\/a>
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\n Semantic Scholar<\/a>
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\n arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Full waveform inversion (FWI) plays a crucial role in the field of geophysics. There has been lots of research about applying deep learning (DL) methods to FWI. The success of DL-FWI relies significantly on the quantity and diversity of the datasets. Nevertheless, existing FWI datasets, like OpenFWI, where sources have fixed locations or identical frequencies, provide limited information and do not represent the complex real-world scene. For instance, low frequencies help in resolving larger-scale structures. High frequencies allow for a more detailed subsurface features. %A single source frequency is insufficient to describe subsurface structural properties. We consider that simultaneously using sources with different frequencies, instead of performing inversion using low frequencies data and then gradually introducing higher frequencies data, has rationale and potential advantages. Hence, we develop three enhanced datasets based on OpenFWI where each source have varying locations, frequencies or both. Moreover, we propose a novel deep operator network (DeepONet) architecture Inversion-DeepONet for FWI. We utilize convolutional neural network (CNN) to extract the features from seismic data in branch net. Source parameters, such as locations and frequencies, are fed to trunk net. Then another CNN is employed as the decoder of DeepONet to reconstruct the velocity models more effectively. Through experiments, we confirm the superior performance on accuracy and generalization ability of our network, compared with existing data-driven FWI methods.<\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

\u0648\u0627\u0631\u0648\u0646\u06af\u06cc \u0645\u0648\u062c \u06a9\u0627\u0645\u0644 (FWI) \u0646\u0642\u0634 \u0645\u0647\u0645\u06cc \u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 \u0698\u0626\u0648\u0641\u06cc\u0632\u06cc\u06a9 \u062f\u0627\u0631\u062f.\u062a\u062d\u0642\u06cc\u0642\u0627\u062a \u0632\u06cc\u0627\u062f\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0631\u0648\u0634\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 (DL) \u062f\u0631 FWI \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u0627\u0633\u062a.\u0645\u0648\u0641\u0642\u06cc\u062a DL-FWI \u0628\u0647 \u06a9\u0645\u06cc\u062a \u0648 \u062a\u0646\u0648\u0639 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0633\u06cc\u0627\u0631 \u0645\u062a\u06a9\u06cc \u0627\u0633\u062a.\u0628\u0627 \u0627\u06cc\u0646 \u0648\u062c\u0648\u062f \u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc FWI \u0645\u0648\u062c\u0648\u062f \u060c \u0645\u0627\u0646\u0646\u062f OpenFWI \u060c \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0645\u0646\u0627\u0628\u0639 \u062f\u0627\u0631\u0627\u06cc \u0645\u06a9\u0627\u0646 \u0647\u0627\u06cc \u062b\u0627\u0628\u062a \u06cc\u0627 \u0641\u0631\u06a9\u0627\u0646\u0633 \u0647\u0627\u06cc \u06cc\u06a9\u0633\u0627\u0646 \u0647\u0633\u062a\u0646\u062f \u060c \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0645\u062d\u062f\u0648\u062f\u06cc \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u0646\u062f \u0648 \u0635\u062d\u0646\u0647 \u067e\u06cc\u0686\u06cc\u062f\u0647 \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u0646\u0645\u06cc \u062f\u0647\u0646\u062f.\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u060c \u0641\u0631\u06a9\u0627\u0646\u0633 \u0647\u0627\u06cc \u067e\u0627\u06cc\u06cc\u0646 \u062f\u0631 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\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 OpenFWI \u062a\u0648\u0633\u0639\u0647 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0645\u0646\u0628\u0639 \u062f\u0627\u0631\u0627\u06cc \u0645\u06a9\u0627\u0646 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u060c \u0641\u0631\u06a9\u0627\u0646\u0633 \u0647\u0627 \u06cc\u0627 \u0647\u0631 \u062f\u0648 \u0627\u0633\u062a.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c \u0645\u0627 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u062c\u062f\u06cc\u062f \u0627\u067e\u0631\u0627\u062a\u0648\u0631 \u0639\u0645\u06cc\u0642 (DeepOnet) \u0645\u0639\u0645\u0627\u0631\u06cc \u0648\u0627\u0631\u0648\u0646\u06af\u06cc-\u0639\u0645\u06cc\u0642 \u0628\u0631\u0627\u06cc FWI \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0645\u0627 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc Convolutional (CNN) \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0644\u0631\u0632\u0647 \u0627\u06cc \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0634\u0627\u062e\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u0646\u0628\u0639 \u060c \u0645\u0627\u0646\u0646\u062f \u0645\u06a9\u0627\u0646 \u0647\u0627 \u0648 \u0641\u0631\u06a9\u0627\u0646\u0633 \u0647\u0627 \u060c \u0628\u0647 \u062a\u0646\u0647 \u062e\u0627\u0644\u0635 \u062a\u063a\u0630\u06cc\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f.\u0633\u067e\u0633 CNN \u062f\u06cc\u06af\u0631\u06cc \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0631\u0645\u0632\u06af\u0634\u0627\u06cc\u06cc DeepOnet \u0628\u0631\u0627\u06cc \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u0645\u062f\u0644\u0647\u0627\u06cc \u0633\u0631\u0639\u062a \u0628\u0647 \u0637\u0648\u0631 \u0645\u0624\u062b\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.\u0627\u0632 \u0637\u0631\u06cc\u0642 \u0622\u0632\u0645\u0627\u06cc\u0634\u0627\u062a \u060c \u0645\u0627 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u062a\u0631 \u0631\u0627 \u062f\u0631 \u0645\u0648\u0631\u062f \u0635\u062d\u062a \u0648 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u062a\u0639\u0645\u06cc\u0645 \u0634\u0628\u06a9\u0647 \u062e\u0648\u062f \u060c \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 \u0631\u0648\u0634 \u0647\u0627\u06cc FWI \u0645\u0648\u062c\u0648\u062f \u062f\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f \u060c \u062a\u0623\u06cc\u06cc\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Inversion-DeepONet: A Novel DeepONet-Based Network with Encoder-Decoder for Full Waveform Inversion \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 […]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"open","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}}},"product_cat":[21],"product_tag":[],"class_list":{"0":"post-62737","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-21","7":"pmpro-has-access","8":"desktop-align-left","9":"tablet-align-left","10":"mobile-align-left","12":"first","13":"instock","14":"downloadable","15":"shipping-taxable","16":"purchasable","17":"product-type-simple"},"yoast_head":"\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Inversion-Deeponet: \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 Deeponet \u0631\u0645\u0627\u0646 \u0628\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u062f\u06a9\u0648\u0631\u0627\u0633\u06cc\u0648\u0646 \u0628\u0631\u0627\u06cc \u0648\u0627\u0631\u0648\u0646\u06af\u06cc \u06a9\u0627\u0645\u0644 \u0634\u06a9\u0644 - 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